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 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.

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 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.

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.

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 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 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 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 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 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 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 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 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 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 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.

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 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 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 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 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 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.

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 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 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 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 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.

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).

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).

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.

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).

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 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 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 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 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 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.