Internship Openings

MERL is looking for research interns to conduct research into novel architectures for "small" large generative models. We are currently exploring 0.5 - 2 billion parameter language models, text-to-image models and text-to-video models. Interesting research directions include (a) efficient learning for such models that improves the pareto front of current scaling laws for these sizes, (b) enhancing current transformer-based architectures, and (c) new architectural paradigms beyond transformers such as incorporating explicitly temporal designs. Prior experience with machine learning/computer vision/natural language processing research, and proficiency in building and experimenting with machine learning models using a framework like PyTorch are required. Candidates well into their PhD program with publications in top-tier machine learning, natural language processing or computer vision venues, ideally connected to building generative models, are strongly preferred. Candidates are also expected to collaborate with MERL researchers for preparing manuscripts for scientific publications based on the results obtained during the internship. Duration of the internship is 3 months with a flexible start date.

Required Specific Experience

• Research experience with recent vision and text generative models
• Deep understanding of neural network architectures
• Proficiency in machine learning frameworks like PyTorch

We seek a highly motivated intern to conduct original research in generative models for instructional 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 novel models, conduct experiments, and prepare results for publication. The candidate should be a PhD student (or recent graduate) 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 video generation. Start date is flexible; duration should be at least 3 months.

Required Specific Experience

• Experience with video diffusion models, LLMs, and Vision-and-Language Models.
• Experience developing and implementing new models in PyTorch
• 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.
• Ph.D. student in computer vision or a related field.

MERL is looking for a highly motivated intern to work on an original research project on multimodal learning, such as audio-visual learning, using limited labeled data. A strong background in computer vision and deep learning is required. Experience in audio-visual (multimodal) learning, weakly/self-supervised learning, continual learning, and large (vision-) language models 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 such as Pytorch. The intern will collaborate with MERL researchers to develop and implement novel 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 date are flexible, but the internship is expected to last for at least 3 months.

Required Specific Experience

• Prior publications in top-tier computer vision and/or machine learning venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS or AAAI.
• Knowledge of the latest self-supervised and weakly-supervised learning techniques.
• Experience with Large (Vision-) Language Models.
• Proficiency in scripting languages, such as Python, and deep learning frameworks such as PyTorch or Tensorflow.

MERL is looking for a highly motivated intern to work on an original research project in rendering dynamic scenes from novel views. A strong background in 3D computer vision and/or computer graphics is required. Experience with the latest advances in volumetric rendering, such as neural radiance fields (NeRFs) and Gaussian Splatting (GS), is desired. 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 candidate will collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The position is available for graduate students on a Ph.D. track or those that have recently graduated with a Ph.D. Duration and start date are flexible but the internship is expected to last for at least 3 months.

Required Specific Experience

• Prior publications in top computer vision/graphics and/or machine learning venues, such as CVPR, ECCV, ICCV, SIGGRAPH, 3DV, ICML, ICLR, NeurIPS or AAAI.
• Experienced in the latest novel-view synthesis approaches such as Neural Radiance Fields (NeRFs) or Gaussian Splatting (GS).
• Proficiency in coding (particularly scripting languages like Python) and familiarity with deep learning frameworks, such as PyTorch or Tensorflow.

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 not limited to): open-vocabulary visual-LiDAR object detection and recognition, domain adaptation or generalization in visual-LiDAR object detection, data-efficient methods for visual-LiDAR object detection, 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 venues, 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.

Required Specific Experience

• Experience with Python, PyTorch, and datasets with both images and LiDAR (e.g. the nuScenes dataset).

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.

Required Specific Experience

• Ph.D. student in computer vision or related field.
• Strong programming skills in Python and Pytorch.
• 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.

MERL is looking for a self-motivated intern to research on problems at the intersection of multimodal large language models and neural algorithmic reasoning. An ideal intern would be a Ph.D. student with a strong background in machine learning and computer vision. The candidate must have prior experience with training multimodal LLMs for solving vision-and-language tasks. Experience in participating and winning mathematical Olympiads is desired. Publications in theoretical machine learning venues would 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.

Required Specific Experience

• Experience with training large vision-and-language models
• Experience with solving mathematical reasoning problems
• Experience with programming in Python using PyTorch
• Enrolled in a PhD program
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

MERL is looking for a self-motivated intern to develop a simulation platform to train vision-and-language models for dynamic human-robot interaction. The ideal intern must have a 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 is desired. The intern is expected to collaborate with researchers in the computer vision team at MERL to develop algorithms and prepare manuscripts for scientific publications.

Required Specific Experience

• Experience in designing novel realistic 3D interactive scenes for robot learning
• Experience with extending vision-based embodied AI simulators
• Strong foundations in machine learning and programming
• Foundations in optimization, specifically scheduling algorithms, would be a strong plus.
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.)
• Must be enrolled in a graduate program, ideally towards a Ph.D.

MERL is looking for a self-motivated intern to work on anomaly localization in industrial inspection setting using computer vision. The relevant topics in the scope include (but 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. The candidates with experiences of image anomaly localization in industrial inspection settings (e.g., MVTec-AD or VisA datasets) and usage of large foundation models 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 venues, 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.

Required Specific Experience

• Experience with Python, PyTorch, and large foundation models (e.g. CLIP, ALIGN, etc.).

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 designing synthetic scenes (e.g., 3D games) using popular graphics software, embodied AI, large language models, reinforcement learning, and the use of 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 at MERL to develop algorithms and prepare manuscripts for scientific publications.

Required Specific Experience

• Experience in designing 3D interactive scenes
• Experience with vision based embodied AI using simulators (implementation on real robotic hardware would be a plus).
• Experience training large language models on multimodal data
• Experience with training reinforcement learning algorithms
• Strong foundations in machine learning and programming
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

We are seeking graduate students interested in helping advance the fields of sound event detection/localization, anomaly detection, and physics informed deep learning for machine sounds. The interns will collaborate with MERL researchers to derive and implement novel algorithms, record data, conduct experiments, integrate audio signals with other sensors (electrical, vision, vibration, etc.), 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, physics informed machine learning, outlier detection, and unsupervised learning.

Multiple positions are available with flexible start dates (not just Spring/Summer but throughout 2025) and duration (typically 3-6 months).

We are seeking graduate students interested in helping advance the fields of generative audio, source separation, speech enhancement, spatial audio, 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, spatial audio reproduction, probabilistic modeling, deep generative modeling, and physics informed machine learning techniques (e.g., neural fields, PINNs, sound field and reverberation modeling).

Multiple positions are available with flexible start dates (not just Spring/Summer but throughout 2025) and duration (typically 3-6 months).

We are looking for a graduate student interested in helping advance the field of multimodal scene understanding, focusing 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).

Required Specific Experience

• Experience with ROS2, C/C++, Python, and deep learning frameworks such as PyTorch are essential.

We are seeking graduate students interested in helping advance the fields of universal audio compression and generation. We aim to build a single generative model that can perform multiple audio generation tasks conditioned on multimodal context. 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 Ph.D. students with experience in some of the following: deep generative modeling, large language models, neural audio codecs. The internship typically lasts 3-6 months.

MERL is looking for a self-motivated intern to work on multi-modal sensor fusion for health condition monitoring and predictive maintenance of motor drive systems. 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. Experience in motor drive system is a plus. The intern is expected to collaborate with MERL researchers to collect data, explore multi-modal data relationship, and prepare manuscripts for publications. The total duration is anticipated to be 3 months and the start date is flexible.

Required Specific Experience

• Experience with multi-modal sensor fusion.

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 about 3 months.

MERL is seeking a motivated and qualified individual to work on physics-informed scientific machine learning algorithms for problems governed by partial differential equations (PDEs). The ideal candidate will be a PhD student in engineering, computer science, or related fields with a solid background in scientific machine learning for PDEs. Preferred skills include knowledge of physics-informed neural networks, operator learning, nonlinear dimensionality reduction, and diffusion models. Strong coding abilities in Python and a popular deep learning framework such as Pytorch are essential. Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.

MERL is seeking a motivated and qualified individual to work on data-driven estimation and control of high-dimensional dynamical systems, with applications in indoor airflow optimization. The ideal candidate will be a PhD student in engineering or related fields with a solid background in estimation, control, and dynamical systems theory. Preferred skills include knowledge of reinforcement learning, data-driven control, nonlinear control, reduced-order modeling (ROM), and partial differential equations (PDEs). Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.

MERL is seeking a motivated and qualified individual to work on fast surrogate models for sound emission and propagation from complex vibrating structures, with applications in HVAC noise reduction. The ideal candidate will be a PhD student in engineering or related fields with a solid background in frequency-domain acoustic modeling and numerical techniques for partial differential equations (PDEs). Preferred skills include knowledge of the boundary element method (BEM), data-driven modeling, and physics-informed machine learning. Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.

MERL seeks graduate students passionate about deep learning and energy systems to contribute to the development of deep time-series forecasting models for real building energy data. The work will involve multi-domain research including deep learning model development, time-series analysis, and possibly integration with energy management systems. The methods will be implemented and evaluated using real-world datasets. The results of the internship are expected to be published in top-tier machine learning and energy systems conferences and/or journals.

Exact start date is flexible (most likely Summer 2025), with an expected duration of 3-6 months, depending on agreed scope and intermediate progress.

Required Specific Experience:

• Current or past enrollment in a PhD program in Electrical Engineering, Computer Science, or a related field with a focus on Machine Learning or Energy Systems.
• 2+ years of research experience in at least some of the following areas: deep learning, time-series analysis, probabilistic machine learning, energy systems modeling.
• PyTorch fluency.
• Familiarity with real-world data wrangling.
• Experience with time-series data visualization and analysis tools.

Strong Pluses:

• Familiarity with transformer-based time-series forecasting methodologies e.g. TFT or time-series foundation models.
• Familiarity with adaptation mechanisms e.g. fine-tuning, meta-learning.

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, PyTorch, and OpenCV are required for the position. Some experience with ROS2 and 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 duration of the internship is 3 to 5 months, with a flexible starting date.

Required Specific Experience

• Python, PyTorch, OpenCV

MERL is looking for a highly motivated individual to work on whole-body dexterous manipulation. The research will develop robot motor skills for whole-body, dexterous manipulation using optimization and/or learning algorithms. The ideal candidate should have experience in either one or multiple of the following topics: Optimization Algorithms for contact systems, Reinforcement Learning, control through contacts, and Behavioral cloning. Senior PhD students in robotics and engineering with a focus on contact-rich manipulation are encouraged to apply. Prior experience working with physical robotic systems (and vision and tactile sensors) is required as results need to be implemented on a physical hardware. Good coding skills in Python ML libraries like PyTorch etc. and/or relevant Optimization packages is required. A successful internship will result in submission of results to a peer-reviewed robotics journal in collaboration with MERL researchers. The expected duration of internship is 4-5 months with start date in May/June 2025. This internship is preferred to be onsite at MERL.

Required Specific Experience

• Prior experience working with physical hardware system is required.
• Prior publication experience in robotics venues like ICRA,RSS, CoRL.

MERL seeks graduate students passionate about robotics to contribute to the development of a framework for legged robots with manipulator arms to collaborate with human in executing various tasks. The work will involve multi-domain research including planning and control, manipulation, and possibly vision/perception. The methods will be implemented and evaluated in high performance simulators and (time-permitting) in actual robotic platforms. The results of the interns are expected to be published in top-tier robotic conferences and/or journal.

The internship should start in January 2025 (exact date is flexible) with an expected duration 3-6 months depending on agreed scope and intermediate progress.

Required Specific Experience

• Current/Past enrollment in a PhD program in Mechanical, Aerospace, Electrical Engineering, with a concentration in Robotics
• 2+ years of research in at least some of: machine learning, optimization, control, path planning, computer vision
• Experience in design and simulation tools for robotics such as ROS, Mujoco, Gazebo, Isaac Lab
• Strong programming skills in Python and/or C/C++

Additional Desired Experience

• Development of planning and control methods in robotic hardware platforms
• Acquisition and processing of multimodal sensor data, including force/torque and proprioceptive sensors
• Prior experience in human-robot interaction, legged locomotion, mobile manipulation

MERL is seeking a highly motivated and qualified intern to work on anomaly detection for operational technology security. The ideal candidate would have significant research experience in anomaly detection, machine learning, and cybersecurity for operational technology. A mature understanding of modern machine learning methods, proficiency with Python and PyTorch, and a relevant research publication history are expected. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. The expected duration is for 3 months with flexible start dates (but ideally in December or early January).

Required Specific Experience

• Proficiency with PyTorch framework.
• Research publications in machine learning and anomaly detection.

MERL is excited to announce an internship opening for a talented researcher to join our team. We are looking for an individual to contribute to cutting-edge research in human-machine interfaces (HMI) using multi-modal bio-sensors. This is an exciting opportunity to make a real impact in the field of human-machine interaction and biosignal processing, with the aim of publishing at leading research venues.

Ideal Candidate:

• Experienced PhD student or post-graduate researcher
• Strong background in brain-machine interface (BMI)
• Proficient in deep learning and mixed reality (XR)
• Skilled in robot manipulation, bionics, and bio sensing
• Digital modeling of human and environment
• Hands-on experience in Unity3d, ROS, OpenBCI, and XR headsets

If you are passionate about advancing technology in these areas, we encourage you to apply and be part of our innovative research team!

MERL is excited to announce an internship opportunity in the field of Quantum Machine Learning (QML) and Quantum AI (QAI). We are seeking a highly motivated and talented individual to join our research team. This is an exciting opportunity to make a real impact in the field of quantum computing and AI, with the aim of publishing at leading research venues.

Responsibilities:

• Conduct cutting-edge research in quantum machine learning.
• Collaborate with a team of experts in quantum computing, deep learning, and signal processing.
• Develop and implement algorithms using PyTorch and PennyLane.
• Publish research results at leading research venues.

Qualifications:

• Currently pursuing a PhD or a post-graduate researcher in a relevant field.
• Strong background and solid publication records in quantum computing, deep learning, and signal processing.
• Proficient programming skills in PyTorch and PennyLane are highly desirable.

What We Offer:

• An opportunity to work on groundbreaking research in a leading research lab.
• Collaboration with a team of experienced researchers.
• A stimulating and supportive work environment.

If you are passionate about quantum machine learning and meet the above qualifications, we encourage you to apply. Please submit your resume and a brief cover letter detailing your research experience and interests. Join us at MERL and contribute to the future of quantum machine learning!

We are on the lookout for passionate and skilled interns to join our cutting-edge research team focused on developing efficient machine learning techniques for sustainability. This is an exciting opportunity to make a real impact in the field of AI and environmental conservation, with the aim of publishing at leading AI research venues.

What We're Looking For:

• Advanced research experience in generative models and computationally efficient models
• Hands-on skills for large language models (LLM), vision language models (VLM), large multi-modal models (LMM), foundation models (FoMo)
• Deep understanding of state-of-the-art machine learning methods
• Proficiency in Python and PyTorch
• Familiarity with various deep learning frameworks
• Ph.D. candidates who have completed at least half of their program

Internship Details:

• Duration: approximately 3 months
• Flexible start dates available
• Objective: publish research results at leading AI research venues

If you are a highly motivated individual with a passion for applying AI to sustainability challenges, we want to hear from you! This internship offers a unique chance to work on meaningful projects at the intersection of machine learning and environmental sustainability.

MERL is seeking a highly motivated and qualified intern to conduct research on IoT network anomaly detection and analysis. The candidate is expected to develop innovative anomaly detection technologies that can proactively detect and analyze network failure in large-scale IOT networks. The candidate should have knowledge of LLM/ML and anomaly detection. Knowledge of network log analysis and network protocol 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.

The responsibilities of this intern position include (i) research on anomaly detection in large-scale IoT networks; (ii) develop proactive network anomaly detection and analysis technologies; (iii) simulate and analyze the performance of developed technology.