Postdoctoral Researcher on AI Agents, Autonomous Decision-Making & Artificial Intelligence

AI Agents, Autonomous Decision-Making & Artificial Intelligence - Postdoctoral Researcher

The research group of Dr. Saptarashmi Bandyopadhyay ( Assistant Professor of Computer Science, at the CUNY City College of New York and CUNY Graduate Center, is inviting applications for a postdoctoral position in AI agents for autonomous decision-making, especially in Real-World Engineering and Scientific Discovery applications. Dr. Bandyopadhyay has grant awards with NSF, DoE, DoW, and NYSERDA along with research collaborations with Industry like NVIDIA, Google, Microsoft, IBM, Amazon, Anthropic and National Labs like AFRL, BNL, ORNL, LLNL, ONLR, LBNL, SLAC, ARNL based on his previous research experience at Google DeepMind.

Dr. Bandyopadhyay is the Co-PI and AI Lead of AI BIOME, a NSF PCL Node accelerating Autonomous Scientific Discovery Research where robots perform high-throughput experiments to create biomaterials, assisting disease cure, battery design, bioplastics, catalysts, energy harvesters among other impactful physical/chemical/biological discoveries in Autonomous Sciences and Engineering. 

We seek highly motivated candidates with research expertise and a stellar publication record at major AI conferences and journals in one or more of the following areas: AI Agents, Robotics, Game Theory, Deep Reinforcement Learning, Multiagent Systems, Control Algorithm, Imitation Learning, Continual Learning, Evolutionary Learning, AR/VR/XR, AI for Science (AI4Sci), Energy-Efficient AI, Quantum Control, Autonomous Vehicles and Drones, and Smart Grids. Applicants will help pioneer autonomous discovery with improved coordination, where multiple AI agents generate hypotheses, propose designs, autonomously plan and execute multi-step trustworthy experiments with robots, and refine themselves in real time through exploration. Applicants will not just improve empirical capabilities but also answer theoretical computer science questions like how much data is needed for optimal learning, how to generalize and bound the agents’ behavior, explore robustness, find out how to make the multiagent systems more controllable, energy-efficient, memory-efficient and network-efficient. 

Qualifications

Candidates should hold a PhD in Computer Science, Machine Learning, Robotics, Electrical and Computer Engineering, or related disciplines by the start date. 

Candidates should have an experience or strong interest in developing AI/ML algorithms and methods for Single-Agent and Multi-Agent Autonomous Decision Making (such as reinforcement learning, imitation learning, game theory, control algorithms, large multimodal models (VLMs/VLAs), or Bayesian optimization) to autonomously plan, reason, allocate, navigate, and explore physical scientific and engineering discovery, laboratory automation, drone swarm control, autonomous vehicle navigation or experimental control.

Candidates should have the ability to work independently while contributing effectively to a highly collaborative, multidisciplinary team environment. A strong commitment to responsible research practices, reproducibility, research ethics and clear communication of autonomous behavior in scientific settings is expected.

The start date is flexible, with preference for candidates able to begin in Oct. 2026. Review of applications will begin immediately and continue until filled.

Applicants should apply directly through this job posting. The following materials are required:

• CV including publication list;
• Coding Excerpts in the open-source
• Github/HuggingFace/Relevant open-source code and data)
• Brief statement of research interests;
• Contact information for 2–3 references.

Salary and Work Arrangement

• Salary Range: $60,000 - $70,000 
• Work Arrangement: On-Site at the CCNY campus in the West Harlem Innovation Triangle, with close coordination in CUNY GC and CUNY ASRC.
• Appointment Period: One year
• Work Authorization: Work Authorization in the US is required as per CUNY CCNY and Project Funding rules
• Appointment Type: Full-time (Renewable based on performance / funding)