Artificial Intelligence

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 looking for a highly motivated and qualified intern to work on methods for task-oriented 6-dof grasp pose detection using vision and tactile sensing. The objective is to enable a robot to identify multiple 6-DoF grasp poses tailored to specific tasks, allowing it to effectively grasp and manipulate objects. The ideal candidate would be a Ph.D. student familiar with the state-of-the-art methods for robotic grasping, object tracking, and imitation learning. This role involves developing, fine-tuning and deploying models on hardware. The successful candidate will work closely with MERL researchers to develop and implement novel algorithms, conduct experiments, and publish research findings at a top-tier conference. Start date and expected duration of the internship is flexible. Interested candidates are encouraged to apply with their updated CV and list of relevant publications.

Required Specific Experience

• Prior experience in robotic grasping
• Experience in Machine Learning
• Excellent programing skills

MERL is looking for a highly motivated individual to work on learning algorithms for generalist robotic manipulation agent. In this internship we will be working on complex, assembly tasks. The research will develop novel learning-based algorithms for assembly tasks such as tight-tolerance insertion. The ideal candidate should have experience in either one or multiple of the following topics: Deep Reinforcement learning, Diffusion Models/Policies and Force Control for Manipulation. Senior PhD students in machine learning and engineering with a focus on Reinforcement/Imitation Learning and Manipulation are encouraged to apply. Prior experience working with physics engines like Mujoco, Isaac Gym, etc. is required. Prior experience working with physical robots as well vision and tactile sensors is required. The developed algorithm needs to be implemented on the real system. 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 expected start date in Sept 2025. This internship is preferred to be onsite at MERL.

Required Specific Experience

• Experience working with physical robot and sensors like cameras, tactile and F/T is a must.
• Prior experience with learning algorithms is a must.