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 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 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 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 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 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.

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.