MERL is looking for a highly motivated individual to develop planning and control algorithms for multi-agent systems for resource monitoring. The ideal candidate has experience in multi-agent motion planning and data-driven, sequential decision-making. The ideal candidate will have published in one or more of these topics: planning over discrete spaces, statistical estimation and hypothesis testing, reinforcement learning, and planning and control of aerial and ground robots. The candidate should be proficient in Python. Additional knowledge of ROS and C/C++ and demonstrable experience in ground and aerial robots are a plus. The minimum duration of the internship is 3 months; the start time is Summer/Fall 2024.

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.

MERL is seeking a highly motivated candidate to collaborate with the Control for Autonomy team in research on control and estimation for quadrotors. The ideal candidate is expected to be working towards a PhD with emphasis on control or related areas, and it is a merit to have interest and background in one or several of: experimentation and research on quadrotors in general and Crazyflies in particular, Lyapunov stability theory, statistical estimation theory, and visual-inertial SLAM. Good programming skills in MATLAB, ROS, Python, are required and knowledge of C is a merit. The expected duration of the internship is 3 months with a start date of late spring or early summer of 2024.

MERL is looking for a highly motivated individual to develop and validate map building algorithms for autonomous mobile robots. The ideal candidate will have published in one or more of these topics: planning and control of ground robots, map building, (visual) SLAM, and sensor fusion. The candidate should be proficient in ROS and C/C++, familiar with Python, and has demonstrable experience working with mobile robots. The minimum duration of the internship is 3 months; the start time is Summer/Fall 2024.

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control algorithms accounting for perception and sensing uncertainty, e.g., the surrounding environment of a vehicle. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible of: predictive control algorithms for linear and nonlinear systems, stochastic constrained control, e.g., chance constraints, stochastic optimization, statistical estimation, perception system modeling, Bayesian inference, and vehicle modeling and control. Good programming skills in MATLAB, Python or C/C++ are required. The expected start of of the internship is flexible, with duration of 3--6 months.

MERL is looking for a highly motivated and qualified individual to work on tailored computational algorithms for optimization-based motion planning and predictive control applications in autonomous systems (vehicles, mobile robots). The ideal candidate should have experience in either one or multiple of the following topics: convex and non-convex optimization, stochastic predictive control (e.g., scenario trees), interaction-aware motion planning, machine learning, learning-based model predictive control, mathematical programs with complementarity constraints (MPCCs), optimal control, and real-time optimization. PhD students in engineering or mathematics, especially with a focus on research related to any of the above topics are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3 months, and the start date is flexible.

MERL is seeking a highly motivated candidate to collaborate with the Control for Autonomy team in research on developing estimation algorithms for GNSS positioning. The ideal candidate is expected to be working towards an MSc or PhD with emphasis on GNSS positioning, and it is a merit to have interest and background in one or several of: factor graph optimization, statistical estimation theory, inertial navigation systems, Kalman filtering, hands-on experience with RTKlib or similar software. Good programming skills in MATLAB are required and knowledge of C is a merit. The expected duration of the internship is 3 months with a start date of late spring or early summer of 2024.

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on trajectory generation and motion planning for heterogenous teams of mobile robots, including drones and ground vehicles, with performance and safety guarantees. The ideal candidate is expected to be working towards a PhD with strong emphasis in planning and control, and to have interest and background in as many as possible of: predictive control algorithms for linear and nonlinear systems, set-based methods in control (reachability, invariance), stochastic control for uncertain systems, SLAM and vision-based planning and control. Good programming skills in MATLAB, Python or C/C++ are required. The expected start of of the internship is flexible, with duration of 3--6 months.

MERL is looking for a highly motivated and qualified individual to work on optimization-based algorithms for coordination, scheduling, and motion planning of automated ground robots in uncertain surrounding environments. The ideal candidate should have experience in either one or multiple of the following topics: formulation of mixed-logic constraints as mixed-integer programs, connected vehicles and coordination, inter-robot collision avoidance, multi-agent scheduling, and traffic control systems. Prior experience with one or multiple traffic and/or multi-vehicle simulators (e.g., SUMO, CarSim, CARLA, etc.) is a plus. PhD students in engineering or mathematics, especially with a focus on research related to any of the above topics are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3 months, and the start date is flexible.

MERL is seeking highly motivated interns for research positions in guidance, navigation, and control of spacecraft. The ideal candidates are PhD students with experience in one or more of the following topics: astrodynamics, the three-body problem, relative motion dynamics, rendezvous, landing, attitude control, orbit control, orbit determination, nonlinear estimation, and optimization-based control. Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are flexible.

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on control and planning algorithms for legged robots for support activities of and collaboration with humans. The ideal candidate is expected to be working towards a PhD with strong emphasis in robotics control and planning and to have interest and background in as many as possible of: motion planning algorithms, control for legged robot locomotions, legged robots, perception and sensing with multiple sensors, SLAM, vision-based control. Good programming skills in Python or C/C++ are required. The expected start of of the internship is flexible, with duration of 3--6 months.

MERL is seeking a talented researcher to collaborate in the development of design algorithms for metalenses that are freeform, multilayer, and broadband. The ideal applicant will have a strong background in the relevant physics & maths, and has some fluency with the topology optimization and EM simulation tools commonly used in metasurface optics. Also desirable: familiarity with machine learning / AI tools and methods.

We are seeking graduate students interested in helping advance the fields of sound event detection/localization and sound anomaly detection. The interns will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which may later become part of the intern''s doctoral work. The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, probabilistic modeling, sequence to sequence models, and deep learning techniques, in particular those involving minimal supervision (e.g., unsupervised, weakly-supervised, self-supervised, or few-shot learning). Multiple positions are available with flexible start date (not just Spring/Summer but throughout 2024) and duration (typically 3-6 months).

MERL is offering internship positions for PhD candidates interested in audio-visual-language multimodal learning. The role involves understanding the complex interplay between sound, visuals, and language, aiming to drive next-generation AI applications. Interns will work closely with a group of researchers at MERL to develop and implement models, with an emphasis on integrating different sensory modalities. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern''s doctoral work. Ideal candidates are senior Ph.D. students in fields such as Audio Machine Learning, Computer Vision, or Natural Language Processing. Experience in multimodal learning is preferable. Good programming skills in Python and knowledge of deep learning frameworks such as PyTorch are essential. Multiple positions are available with flexible start date (not just Spring/Summer but throughout 2024) and duration (typically 3-6 months).

We are seeking graduate students interested in helping advance the fields of generative audio, source separation, speech enhancement, and robust ASR in challenging multi-source and far-field scenarios. The interns will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern''s doctoral work. The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, probabilistic modeling, sequence to sequence models, and generative modeling techniques, in particular those involving minimal supervision (e.g., unsupervised, weakly-supervised, self-supervised, or few-shot learning). Multiple positions are available with flexible start date (not just Spring/Summer but throughout 2024) and duration (typically 3-6 months).

We are looking for a graduate student interested in helping advance the field of multimodal scene understanding, with a focus on scene understanding using natural language for robot dialog and/or indoor monitoring using a large language model. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern''s doctoral work. The ideal candidates are senior Ph.D. students with experience in deep learning for audio-visual, signal, and natural language processing. Good programming skills in Python and knowledge of deep learning frameworks such as PyTorch are essential. Multiple positions are available with flexible start date (not just Spring/Summer but throughout 2024) and duration (typically 3-6 months).

MERL is seeking a highly motivated and qualified intern to collaborate with the Multiphysical Systems (MS) team in research on knowledge transfer methods for deep learning, e.g. meta/transfer learning to be used for system identification using data from real building energy systems. The ideal candidate is expected to be working towards a Ph.D. in applying deep learning to system identification problems, with special emphasis in one or more of: generative modeling, probabilistic deep learning, and learning for estimation/control. Fluency in PyTorch is necessary. Previous peer-reviewed publications in related research topics and/or experience with state-of-the-art generative models for time-series is a plus. Publication of results obtained during the internship is expected. The minimum duration of the internship is 12 weeks; start time is flexible with slight preference towards late Spring/early Summer 2024.

MERL is looking for a highly motivated and qualified candidate to work on modeling for smart sustainable buildings. The ideal candidate will have a strong understanding of modeling renewable energy sources, grid-interactive buildings, occupant behavior, and dynamical systems with expertise demonstrated via, e.g., peer-reviewed publications. Hands-on programming experience with Modelica is preferred. The minimum duration of the internship is 12 weeks; start time is flexible.

MERL is seeking a motivated graduate student to research numerical methods pertaining to the simulation, control, and optimization of large-scale systems. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in numerical methods, control, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months.

MERL is seeking a highly motivated and qualified individual to conduct research in dynamic modeling and simulation of vapor compression systems in the summer of 2024. Knowledge of data-driven modeling techniques is required. Experience with sampling-based control methods is preferred. Experience in working with thermo-fluid systems is a plus. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. Senior Ph.D. students in applied mathematics, chemical/mechanical engineering and other related areas are encouraged to apply. The expected duration of the internship is 3 months, and the start date is flexible.

MERL is looking for a highly motivated and qualified candidate to work on estimation of multi-physical systems governed by sets of differential algebraic equations (DAEs). The research will involve study and development of estimation approaches for large-scale nonlinear systems, e.g., vapor compression cycles, with limited sensor availability. The ideal candidate will have a strong background in one or multiple of the following topics: nonlinear control and estimation, sensor selection, optimization, and active learning; with expertise demonstrated via, e.g., peer-reviewed publications. Prior programming experience in Julia/Modelica is a plus. Senior PhD students in mechanical, electrical, chemical engineering or related fields are encouraged to apply. The typical duration of internship is 3 months, and the start date is flexible.

MERL is seeking a motivated and qualified individual to conduct research on transfer learning for fault diagnosis, to be used for industrial applications especially electric machine fault diagnosis and predictive maintenance. Ideal candidates are Ph.D. students with a solid background and publication record in one or more research areas: fault diagnosis, statistical machine learning, transfer learning and domain adaptation, and electric motors. Strong programming skills using Python/PyTorch are expected. Knowledge and background in electric machines related research is a strong plus. Start date for this internship is flexible and the duration is typically 3 months.

MERL is seeking a highly motivated and qualified individual to conduct research in the intersection of control theory and learning to achieve high precision motion with guaranteed safety and robustness. The ideal candidate should have solid backgrounds in mechanics, uncertainty quantification, control theory, and reinforcement learning, and strong coding skills. Prior experience on ultra-high precision motion control system and visual servoing is a big plus. Ph.D. students in mechatronics and control are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

MERL is seeking a motivated and qualified intern to conduct research on machine learning based electric motor design and optimization. Ideal candidates should be Ph.D. students with a solid background and publication record in electric machine design, optimization, and machine learning. Hands-on experience with the implementation of optimization algorithms, machine learning and deep learning methods is required. Strong programming skills using Python/PyTorch are expected. Knowledge and experience with electric machine principle, design and finite-element analysis are highly desirable. Start date for this internship is flexible and the duration is 3-6 months.

MERL is seeing a motivated and qualified individual to conduct research on electric motor design and modeling, with a strong focus on electromagnetic analysis. Ideal candidates should be Ph.D. students with solid background and publication record in one more research area on electric machines: electric and magnetic modeling, new machine design and prototyping, harmonic analysis, fault detection, and predictive maintenance. Research experiences on modeling and analysis of electric machines and fault diagnosis are required. Hands-on experience with new motor design and data analysis techniques are highly desirable. Start date for this internship is flexible and the duration is 3-6 months.

MERL is seeking a highly motivated and qualified individual to conduct research in dynamic model-based robotic system design and control. The ideal candidate should demonstrate solid research record in robotic dynamics and differentiable simulation, motion planning and control, and optimization. Strong coding skill on implementing robotic dynamics and differentiable simulation/optimization using CasADi/PyTorch is a necessity. Ph.D. students in mechanical engineering, robotics, computer science, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

We are seeking a graduate student interested in the research of AI-assisted design of semiconductor devices in general and GaN, SiC and Si IGBT in particular. The interns will collaborate with researchers at MERL and those in Japan to explore and develop new AI input models and methodology, and optimization methods, using both simulated and experimental data for the AI-assisted design of semiconductor devices. The ideal candidates are senior Ph.D. students with experience in semiconductor device physics, device modeling, deep learning, and other machine learning techniques, and the use of TCAD as a simulation tool. Those with deep knowledge of GaN, Si, and SiC devices and applications in RF and power electronics will be great assets. This internship's Start date is flexible and lasts 3-6 months.

MERL is looking for a self-motivated intern to work on sensing data fusion with applicatino to condition monitoring, fault detection, and predictive maintenance. The ideal candidate would be a Ph.D. candidate in electrical engineering or computer science with solid research background in signal processing and machine learning. Background in electric machine, system control and automation is preferred. Proficiency in MATLAB is necessary. The intern is expected to collaborate with MERL researchers to perform simulations, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is anticipated to be 3-6 months and the start date is flexible. This internship requires work that can only be done at MERL.

MERL is looking for a self-motivated intern to work on electric machine monitoring, fault detection, and predictive maintenance. The ideal candidate would be a Ph.D. candidate in electrical engineering or computer science with solid research background in electric machines, signal processing, and machine learning. Proficiency in MATLAB and Simulink is necessary. The intern is expected to collaborate with MERL researchers to perform simulations, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is anticipated to be 3 months and the start date is flexible. This internship requires work that can only be done at MERL.

MERL is seeking an intern to work on blockchain based solutions for factory automation. The ideal candidate will have experience implementing a blockchain network with Hyperledger Fabric, have experience working with smart contracts, and be fluent in C++ and Go. Experience developing applications in an embedded linux environment is highly desirable. We are looking for someone to start as soon as possible and the duration is 3-4 months.

MERL is looking for a self-motivated intern to work on time sequence data analysis for denoising, feature extraction, and machine learning. The ideal candidate would be a Ph.D. candidate in computer science or electrical engineering with solid research background in signal processing and machine learning. Experience with time sequence data processing is preferred. Proficiency in MATLAB is necessary. The intern is expected to collaborate with MERL researchers to perform simulations, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is anticipated to be 3 months and the start date is flexible. This internship requires work that can only be done at MERL.

MERL is looking for interns for the project of magnetic particle imaging (https://www.mitsubishielectric.com/news/2023/pdf/0907-a.pdf). We expect the intern to (1) build a model that describes the magnetic particle imaging system; (2) implement a few existing reconstruction algorithms and identify their relative strengths; (3) (ideally) develop/identify the algorithm specific for the system, and/or suggest the measurement schemes for image reconstruction. Candidates are expected to have basic understanding of electromagnetic theory and solid skill of coding (Python and/or C++ and/or matlab). Students from physics, mathematics, electrical engineering or related fields are encouraged to apply.

MERL is seeking a motivated and qualified intern to conduct research on shape optimization of electrical machines. The ideal candidate should have a solid background and demonstrated research experience in mathematical optimization methods, including topology optimization, robust optimization, and sensitivity analysis, as well as machine learning methods. Hands-on coding experience with the implementation of topology optimization algorithms and finite-element simulation are desirable. Knowledge and experience with electric machine principle, design and finite-element analysis is a strong plus. Senior Ph.D. students in related expertise are encouraged to apply. Start date for this internship is flexible and the duration is 3-6 months.

MERL is looking for a highly motivated and qualified PhD student in the areas of machine learning and robotics, to participate in research on advanced algorithms for learning control of robots and other mechanisms. Solid background and hands-on experience with various machine learning algorithms is expected, and in particular with deep learning algorithms for image processing and object detection. Exposure to deep reinforcement learning and/or learning from demonstration is highly desirable. Familiarity with the use of machine learning algorithms for system identification of mechanical systems would be a plus, along with background in other areas of automatic control. Solid experimental skills and hands-on experience in coding in Python and PyTorch are required for the position. Some familiarity with classical mechanics and computational physics engines would be helpful, but is not required. The position will provide opportunities for exploring fundamental problems in incremental learning in humans and machines, leading to publishable results. The starting date of the internship is flexible, and applications outside of the peak summer season are encouraged, too.

MERL is looking for a highly motivated and qualified intern to work on human-robot interaction (HRI) research. The ideal candidate would be a Ph.D. student with a strong background in HRI, focusing on robotic manipulation, deep learning, probabilistic modeling, or reinforcement learning. Several topics are available for consideration, including Intent Recognition in Multi-Object Scenes, Shared Autonomy, Cooperative Manipulation, Human-Robot Handovers, and Representation Learning for HRI. Experience working with robotics hardware and physics engine simulators like PyBullet, Issac Gym, or Mujoco is preferred. Proficiency in Python programming is necessary, and experience with ROS is a plus. The successful candidate will collaborate with MERL researchers, and publication of the relevant results is expected. The start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics.

MERL is seeking a highly motivated and qualified intern to work on deep learning for visual feedback in robotic manipulation. The ideal candidate would be a Ph.D. student with a strong background in deep learning and robotic manipulation. Several topics are available for consideration, including Object Pose Estimation, Goal-driven Grasping, Diffusion policy for Industrial Tasks, and Deformable Object Manipulation. The project requires the development of novel algorithms with implementation and evaluation on a robotic platform. Preferred qualifications include experience working with a physics engine simulator like PyBullet, Isaac Gym, or Mujoco, proficiency in Python programming, and experience with ROS. The successful candidate will collaborate with MERL researchers, and publication of relevant results is expected. The start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and a list of publications in related topics

MERL is looking for a self-motivated and qualified candidate to work on Bayesian Optimization algorithms applied to industrial applications. The ideal candidate is a PhD student with experience and peer-reviewed publications in the general field of derivative-free/zeroth-order optimization, preference will be given to candidates who have contributed to theoretical advances or practical application of Bayesian optimization, especially for multi-objective optimization problems. The ideal candidate will have a strong general understanding of numerical optimization and probabilistic machine learning e.g. Gaussian process regression, and is expected to develop, in collaboration with MERL researchers, state of the art algorithms to optimize parameters for industrial processes or control systems. Proficiency in Python is required. An expected outcome of the internship is one or more peer-reviewed publications. The expected duration is 3-4 months, with flexible starting date.

MERL is seeking highly motivated intern to work on the development of novel optimization algorithms. The target applications span a broad range of areas including power systems, control, scheduling, and transportation. Successful candidate will collaborate with MERL researchers to develop and implement new algorithms, conduct experiments, and prepare results for publication. Ideal candidate would be senior PhD student with experience in one or more of the following areas: conic programming, active-set methods, and nixed integer programs. Strong programming skills and fluency in C++ and Python are expected. Prior experience with popular optimization packages such as Ipopt, Gurobi, Cplex is a plus. The duration of the internship is expected to be 3 months. Start date is flexible.

MERL is looking for a highly-motivated individual to work on estimation and control for dexterous contact-rich manipulation using vision and tactile feedback. The research will develop novel estimation and control algorithms for dexterous manipulation tasks such as bimanual assembly and tool manipulation under unexpected contact events using optimization, machine learning, and contact sensing. The ideal candidate should have experience in either one or multiple of the following topics: Visuo-tactile manipulation, (learning-based) contact estimation, in-hand manipulation, tactile perception, computer vision, trajectory optimization through contact, and deep reinforcement learning for contact-rich systems. Senior PhD students in robotics, computer science, and engineering with a focus on robotic manipulation and legged robotics are encouraged to apply. Prior experience working with physical robotic systems with closed-loop control is required as results need to be implemented on a physical hardware. A successful internship will result in submission of results to peer-reviewed conference (e.g., ICRA, IROS, RSS, CoRL) and journals (e.g., TRO, IJRR). Good coding skills in Python and C++ are required. The expected duration of internship is 3-4 months with flexible start dates. This internship is preferred to be onsite at MERL.

MERL is offering a new research internship opportunity in the field of robotic manipulation. The position requires a robotics background, excellent programming skills and experience with Deep RL and Computer Vision. The position is open to graduate students on a PhD track only, and the length of the internship is three months with the possibility of extending if required. The intern is expected to disseminate this research in top tier scientific conferences such as RSS, IROS, ICRA etc., and if applicable, help with filing associated patents. Start and end dates are flexible.

MERL is seeking a highly motivated and qualified individual to conduct research on DER-centric Microgrid Optimization and Control. Ideal candidate should be a senior Ph.D. student with solid background and publication record in any of the following, or related areas: power systems, Microgrid, distribution energy resources, host capacity, optimization, and uncertainty modelling. Hand-on experience on programming using Python, C/C++, or MATLAB is required. The duration of the internship is anticipated to be 3-6 months, and the start date is flexible.

MERL is looking for a self-motivated and qualified candidate to work on human-robot-interaction for manipulation and assembly collaborative scenarios. The ideal candidate is a PhD student and should have experience and records in one or multiple of the following areas. 1) Control, estimation and perception for Robotic manipulation 2) Task and Motion Planning 3) Learning from demonstration algorithms applied to robotic manipulation 4) Machine learning techniques for modeling and control as well as regression and classification problems. 5) Experience in working with robotic systems and familiarity with physics engine simulators like Mujoco, Isaac Gym, PyBullet. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms to solve complex manipulation tasks that involve human and robot collaborations. Proficiency in Python and ROS are required. The expectation is that the research will lead to one or more scientific publications. The expected duration s 3-4 months, with a flexible starting date.

MERL is looking for a self-motivated intern to develop algorithms for time series data analytics, system identification, and deep learning. The ideal candidate would be a senior PhD student with experience in one or more of the following areas: system identification, deep learning, mathematical optimization. Strong programming skills using Python/PyTorch are expected. Experience in developing heuristics for nonconvex optimization problems and building spatial-temporal prediction models would be a plus. The intern is expected to work with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is expected to be 3 months. Start date is flexible.

MERL is looking for a highly motivated individual to work on long-horizon planning and decision making for robotic manipulation using large language models (LLMs) and Hierarchical RL. The research will develop novel algorithms for long-horizon task planning and execution using Hierarchical Reinforcement learning and LLMs. The ideal candidate should have experience in either one or multiple of the following topics: (Deep) Reinforcement learning, Hierarchical RL, LLMs, policy optimization and Markov Decision Processes (MDPs). Senior PhD students in machine learning and engineering with a focus on Reinforcement Learning and robotics are encouraged to apply. Prior experience working with physics engines like Mujoco, Isaac Gym, etc. is required. Prior experience working with Python and ROS is required. A successful internship will result in submission of results to peer-reviewed conference and journals. Good coding skills in Python and state-of-the-art RL environments (e.g., RL Bench) is required. The expected duration of internship is 3-4 months with flexible start dates. This internship is preferred to be onsite at MERL.

MERL is looking for a self-motivated intern to work on the problem of video anomaly detection. The intern will help to develop new ideas for improving the state of the art in detecting anomalous activity in videos. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision and some experience with video anomaly detection in particular. Proficiency in Python programming and Pytorch is necessary. The successful candidate is expected to have published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS or AAAI. The intern will collaborate with MERL researchers to develop and test algorithms and prepare manuscripts for scientific publications. The internship is for 3 months and the start date is flexible.

MERL is looking for a highly motivated intern to work on an original research project in rendering dynamic scenes from novel views, with a potential use-case in outer space settings. A strong background in 3D computer vision and/or computer graphics is required. Experience in the latest advances of deep learning, such as neural radiance fields and Gaussian splatting, is an added plus and will be valued. The successful candidate is expected to have published at least one paper in a top-tier computer vision/graphics or machine learning venue, such as CVPR, ECCV, ICCV, SIGGRAPH, 3DV, ICML, ICLR, NeurIPS or AAAI, and possess solid programming skills in Python and popular deep learning frameworks like Pytorch. The goal would be for such a candidate to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Successful applicants are typically graduate students on a Ph.D. track or recent Ph.D. graduates. Duration and start dates are flexible but internship is expected to last for at least 3 months.

MERL is looking for a highly motivated intern to work on an original research project in 3D representation and reconstruction using implicit neural networks. A strong background in 3D computer vision is required. Experience in deep learning will be valued. The successful candidate is expected to have published or submitted at least one paper in a top-tier computer vision venue, such as CVPR, ECCV, ICCV, NeurIPS, ICLR, and ICML, along with solid programming skills in Python and/or C++. The position is available for graduate students on a Ph.D. track. Duration and start dates are flexible.

MERL is looking for a self-motivated intern to work on problems at the intersection of multimodal large language models and embodied AI in dynamic indoor environments. The ideal candidate would be a PhD student with a strong background in machine learning and computer vision, as demonstrated by top-tier publications. The candidate must have prior experience in audio-visual AI, large language models, and simulators such as Habitat/SoundSpaces. Hands on experience in using animated 3D human shape models (e.g., SMPL and variants) is desired. The intern is expected to collaborate with researchers in computer vision and speech teams at MERL to develop algorithms and prepare manuscripts for scientific publications.

MERL is seeking an intern to conduct research for cloud removal from satellite images. The focus will be on building novel deep learning algorithms for this application. A good candidate is a PhD student with experience in deep learning and computational imaging with a publication record. Prior knowledge and experience in deep image restoration algorithms e.g., deep algorithm unrolling, using deep priors such as diffusion models are strongly preferred. Good Python and Pytorch skills are required. Publication of results in a conference or a journal is expected. The expected duration of the internship is 3 months and the start date is flexible.

MERL is looking for a highly motivated intern to work on an original research project in open-world object detection. A strong background in computer vision and deep learning is required. Experience in the latest advances in object detection, incremental learning, and open-world object detection is an added plus and will be valued. The successful candidate is expected to have published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS or AAAI, and possess solid programming skills in Python and popular deep learning frameworks like Pytorch. The position is available for graduate students on a Ph.D. track. Duration and start dates are flexible but are expected to last for at least 3 months. This internship is preferred to be onsite at MERL’s office in Cambridge, MA

MERL is looking for a self-motivated intern to work on anomaly localization in the industrial inspection setting using computer vision. The relevant topics in the scope include (but are not limited to): cross-view image anomaly localization, how to train one model for multiple views and defect types, how to incorporate large foundation models in image anomaly localization, etc. Candidates with experience in image anomaly localization in industrial inspection settings (e.g., MVTec-AD or VisA datasets) are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.

MERL is looking for a highly motivated intern to work on an original research project that seeks to improve the generalizability of multimodal learning techniques. A strong background in computer vision and deep learning is required. Experience in audio-visual (multimodal) learning and continual learning are an added plus and will be valued. The successful candidate is expected to have published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS or AAAI, and possess solid programming skills in Python and popular deep learning frameworks like Pytorch. The goal would be for such a candidate to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Successful applicants are typically graduate students on a Ph.D. track or recent Ph.D. graduates. Duration and start dates are flexible but the internship is expected to last at least 3 months.

We seek a highly motivated intern to conduct original research in generative models for conditional video generation. We are interested in applications to various tasks such as video generation from text, images, and diagrams. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The candidate should be a PhD student (or postdoc) in computer vision and machine learning with a strong publication record including at least one paper in a top-tier computer vision or machine learning venue such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, AAAI, or TPAMI. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch, and broad knowledge of machine learning and deep learning methods are expected, including experience in the latest advances in conditional video generation. Start date is flexible; duration should be at least 3 months.

MERL is looking for a self-motivated intern to work on visual-LiDAR fused object detection and recognition using computer vision. The relevant topics in the scope include (but are not limited to): domain adaptation or generalization in visual-LiDAR object detection, data-efficient methods for visual-LiDAR object detection, open-set visual-LiDAR object detection and recognition, small object detection with visual-LiDAR input, etc. The candidates with experiences of object recognition in LiDAR are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.

MERL is looking for a highly motivated intern to work on an original research project on visual localization and mapping. A strong background in 3D computer vision is required. Experience in robot vision and/or deep learning will be valued. The successful candidate is expected to have published at least one paper in a top-tier computer vision or robotics venues, such as CVPR, ECCV, ICCV, ICRA, IROS, or RSS, along with solid programming skills in Python and/or C/C++. The position is available for graduate students on a Ph.D. track. Duration and start dates are flexible.

MERL is seeking a highly motivated intern to conduct original research in estimating vital signs such as heart rate, heart rate variability, and blood pressure from video of a person. The successful candidate will use the latest methods in deep learning, computer vision, and signal processing to derive and implement new models, collect data, conduct experiments, and prepare results for publication, all in collaboration with MERL researchers. The candidate should be a Ph.D. student in computer vision with a strong publication record and experience in computer vision, signal processing, machine learning, and health monitoring. The successful candidate is expected to have published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI, and possess strong programming skills in Python and Pytorch. Start date is flexible; duration should be at least 3 months.

MERL is looking for a self-motivated intern to develop a general-purpose simulation platform for generating computer vision datasets defined by downstream tasks. The ideal intern must have strong background in computer graphics, computer vision, and machine learning, as well as experience in using the latest graphics simulation toolboxes and physics engines. Working knowledge of recent multimodal generative AI methods will be a strong plus. The intern is expected to collaborate with researchers in the computer vision team at MERL to develop algorithms and prepare manuscripts for scientific publications.

The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in THz (Terahertz) sensing. Expertise in statistical inference, unsupervised anomaly detection, and deep learning (spatial-temporal representation learning) is required. Previous hands-on experience in THz data analysis is a plus. Familiarity with python and deep learning libraries is a must. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with collaborators, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.

The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in radar perception. Expertise in deep learning-based object detection, multiple object tracking, data association, and representation learning (detection points, heatmaps, and raw radar waveforms) is required. Previous hands-on experience on open indoor/outdoor radar datasets is a plus. Familiarity with the concept of FMCW, MIMO, and range-Doppler-angle spectrum is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.

The Computational Sensing Team at MERL is seeking an intern to work on estimation algorithms for radioactive source localization. The candidate should have experience with statistical modeling and estimation theory. A detailed knowledge of interactions of particles with matter, imaging inverse problems, and/or computed tomography is preferred. Hands-on experience with high-energy physics simulators (e.g., Geant4) is beneficial but not required. Strong programming skills in Python are essential. Publication of the results produced during our internships is expected. The duration is anticipated to be 3-6 months.

The Dynamics team at MERL is seeking a highly motivated, qualified individual to join our internship program in the summer of 2024. The ideal candidate will be a Ph.D. student specializing in engineering, applied mathematics, computer science or similar fields with solid background in estimation, control and dynamical systems theory. Research exposure to one of the following is very desirable but not necessary: reduced-order models (ROMs), reinforcement learning, nonlinear control, PDEs, and robust control. Publication of results obtained during the internship is expected. The starting date is flexible and the internship will last about 12 weeks.

MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.

The Computational Sensing team at MERL is seeking motivated and qualified individuals to assist in the development of computational methods for a variety of sensing applications. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, deep learning for inverse problems, large-scale optimization, blind inverse scattering, radar/lidar/THz imaging, joint communications and sensing, multimodal sensor fusion, object or human tracking, sensing of dynamical systems, or wave-based inversion. Experience with experimentally measured data is desirable. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

The Computational Sensing Team at MERL is seeking an intern to work on estimation algorithms for single-photon lidar. The candidate should have experience with statistical modeling and estimation theory. A detailed knowledge of single-photon detection, lidar, and/or Poisson processes is preferred. Hands-on optics experience is beneficial but not required. Strong programming skills in Python or Matlab are essential. Publication of the results produced during our internships is expected. The duration is anticipated to be 3-6 months.

The Computational Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in integrated sensing and communication (ISAC) with a focus on signal processing, model-based learning, and optimization. Expertise in joint waveform/sequence optimization, integrated ISAC precoder/combiner design, model-based learning for ISAC, and downlink/uplink/active sensing under timing and frequency offsets is highly desired. Familiarity with IEEE 802.11 (ac/ax/ad/ay) standards is a plus but not required. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments using MERL in-house testbed, and prepare results for publication. The expected duration of the internship is 3 months with a flexible start date.

The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that can perform background oriented Schlieren (BOS) tomography. The project goal is to utilize both analytical and learning-based architectures to enable the reconstruction of 3D air flows in an indoor setting from BOS measurements. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, learning-based modeling for imaging, Schlieren tomography, physics informed neural networks. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

The Dynamics team at MERL is seeking a motivated and qualified individual to conduct research in safe robust reinforcement learning (RL). The ideal candidate should have solid background in RL, e.g. Constrained Markov decision processes (CMDPs), and Robust MDPs theories. Knowledge of dynamical system theory and nonlinear control theory is a plus, but not a requirement. Submission of the results produced during the internship is anticipated, e.g., ICML/ICLR/NeurIPS. Duration of the internship is expected to be 3 months. Start date is flexible.

The Dynamics team at MERL is seeking a highly motivated, qualified individual to join our internship program in the summer of 2024. The ideal candidate will be a Ph.D. student specializing in engineering, applied mathematics, computer science or similar fields with solid background in scientific machine learning, and deep learning. Research exposure to one of the following is very desirable but not necessary: Dynamical systems theory, operator learning (DeepONet, FNO, etc.), and Physics-informed Neural Nets (PINNs). Ideal candidate is familiar with PyTorch, TensorFlow, or Jax. Publication of results obtained during the internship is expected. The starting date is flexible and the internship will last about 12 weeks.

MERL is seeking an intern to work on research for human-machine interface with multi-modal bio-sensors. The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in brain-machine interface (BMI), deep learning, mixed reality (XR), remote robot manipulation, bionics, and bio sensing. The expected duration of the internship is 3-6 months, with a flexible start date.

MERL is seeking a highly motivated and qualified intern to carry out research on energy efficient IoT network design methodology. The candidate is expected to develop innovative network technologies to support energy efficient communications among heterogeneous nodes. The candidates should have knowledge of network technologies such as sleep management and path planning and network simulation tools such as ns3. Knowledge of communication standards such as IEEE 802.11 and 3GPP and UAV is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

MERL is seeking highly motivated and qualified interns to work on efficient machine learning techniques. The ideal candidates would have significant research experience in federated learning, generative large language models, and efficient/green AI. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. The expected duration is 3 months long with flexible start dates.

MERL is seeking a highly motivated and qualified intern to work on operational technology security. The ideal candidate would have significant research experience in cybersecurity for operational technology, anomaly detection, robust machine learning, and defenses against adversarial examples. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. The expected duration is 3 months with flexible start dates.

MERL is seeking an intern to work on research for quantum machine learning (QML). The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in quantum computing, deep learning, and signal processing. Proficient programming skills with PyTorch and PennyLane will be additional assets to this position.

MERL is seeking a highly motivated and qualified intern to conduct research on emerging next generation IoT networking. The candidate is expected to develop innovative networking technologies to achieve efficient network traffic delivery in next generation IoT networks with heterogeneous nodes. The candidate should have knowledge of networking protocols such as multi-path UDP/TCP and network simulation tools such as ns3. Knowledge of the optimization such as optimal network path discovery is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

MERL is seeking a highly motivated and qualified intern to work on methods for trustworthy generative AI. The ideal candidate would have significant research experience in trustworthy AI methods for large language models, such as for preventing hallucinations, handling data memorization issues, generation provenance tracking, and/or grounding with world modeling. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Candidates at or beyond the middle of their Ph.D. program, possessing a background in Machine Learning, especially in the context of Natural Language Processing, are strongly encouraged to apply. The expected duration is 3 months with flexible start dates. Join us at MERL and be part of a transformative journey in Generative AI research!

MERL is seeking a highly motivated and qualified intern to work on privacy issues in machine learning. The ideal candidate would have significant research experience in private machine learning methods and defenses against membership inference attacks. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. The expected duration is 3 months with flexible start dates.