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SA1959: Metasurfaces for machine vision
We seek highly qualified candidates for research on co-design and optimization of metasurfaces and machine vision algorithms, with a particular interest in polarization. Strong candidates will have a background in metasurface optics, fluency with FDTD and RCWA simulation tools, and some familiarity with optimization methods used in computer vision and machine learning.
- Research Areas: Applied Physics, Computational Sensing, Machine Learning
- Host: Matt Brand
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ST1967: Deep Learning for Radar Perception
The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in automotive 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 automotive 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.
- Research Areas: Artificial Intelligence, Computational Sensing, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics, Signal Processing
- Host: Perry Wang
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ST1916: Lidar Architectures & Algorithms
MERL is seeking a motivated intern to develop algorithms for novel lidar systems. The ideal candidate would be a senior PhD student with a strong foundation in at least some of the following areas: statistical signal processing, stochastic processes, estimation, sampling theory. Hands-on experience with lidar systems, lasers, photodetectors, or other optics hardware is a bonus. Strong programming skills in MATLAB or Python are essential. Publication of the results produced during our internships is expected. Duration is anticipated to be 3 months.
- Research Areas: Computational Sensing, Electronic and Photonic Devices, Signal Processing
- Host: Joshua Rapp
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ST1762: Computational Sensing Technologies
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.
- Research Areas: Computational Sensing, Signal Processing
- Host: Petros Boufounos
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ST1750: THz (Terahertz) Sensing
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.
- Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Optimization, Signal Processing
- Host: Perry Wang
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ST1763: Technologies for Multimodal Tracking and Imaging
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.
- Research Areas: Computational Sensing, Signal Processing
- Host: Petros Boufounos
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ST1966: Underground Radar Imaging
The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that solve full waveform inversion problems to recovery the material properties and distribution of targets from underground radar imaging modalities such as ground penetrating radar (GPR). The project goal is to utilize both analytical and learning-based architectures to generate detailed maps of underground scenes using radar measurements acquired at the surface. 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, full waveform inversion, seismic imaging, ground penetrating radar. 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.
- Research Areas: Applied Physics, Computational Sensing, Machine Learning
- Host: Hassan Mansour
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ST1968: Integrated Sensing and Communication (ISAC - WLAN Sensing)
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 for WLAN sensing. Expertise in waveform/sequence optimization, integrated precoding for ISAC, and ToF-Doppler-Angle spectrum estimation using Wi-Fi packets is highly desired. Familiarity with IEEE 802.11 (ac/ax/ad/ay) standards is a plus. Knowledge of Wi-Fi-based localization, occupancy sensing, device-free pose/gesture recognition, skeleton tracking, and multi-modal fusion is an asset. 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.
- Research Areas: Artificial Intelligence, Communications, Computational Sensing, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics, Signal Processing
- Host: Perry Wang
- Apply Now