PostDoc Researcher

Research Foundation of The City University of New York • New York, NY

6d • Onsite

About The Position

The AI-Enabled Bio-Inspired Materials discovery Ecosystem (AI-BIOME) at the CUNY City College of New York (CCNY) invites applications for Postdoctoral Researchers. Funded by an NSF grant award, AI-BIOME is a new, public-private Programmable Cloud Laboratory (PCL) Node dedicated to bio-derived, supramolecular, and bioinspired materials design, discovery, and characterization. This position offers a unique opportunity to work at the intersection of AI agent-enabled reasoning and hands-on autonomous experimentation. You will help pioneer closed-loop discovery, 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 scientific exploration.

Requirements

• Ph.D. in Computer Science, Machine Learning, Robotics, Electrical and Computer Engineering, Computational Materials Science, Chemical Engineering, Computational Physics, Bioengineering, Computational Chemistry, or a related discipline.
• 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 explore physical scientific discovery, laboratory automation, or experimental control.
• Experience or strong interest in hardware development, focusing on building complete hardware-software systems to accelerate discovery in material and interfacial science, including robotic platforms, embedded chemical engineering systems and automated analytical pipelines tightly integrated with AI agents and cloud-based orchestration.
• 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.

Responsibilities

• Conduct research integrating AI-driven experimental design with automated materials synthesis and characterization, emphasizing trustworthiness, reliability, robustness, and transparency in AI decisions that guide embedded experimental workflows.
• Build robust and efficient algorithms to control AI Agents that can learn generalizable plans to design and synthesize bio-inspired materials, adapt to mistakes in the plans autonomously, steer navigation and accelerate discovery and exploration at a distributed scale. This includes developing planning, reasoning, navigation and exploration algorithms (e.g., efficient RL, imitation learning approaches like behavioral cloning, game theoretic equilibriums, hierarchical controllers, causal and probabilistic reasoning modules) that ensure trustworthy behavior of agents in autonomous laboratory environments and enable long-horizon, multi-step high-throughput experimental campaigns.
• Develop new workflows within the robotic platforms, focusing on training new capabilities to the robots.
• Design and validate autonomous laboratory protocols in which embedded AI agents coordinate liquid-handling robots, robotic carts, analytical instruments, and cloud infrastructure in a closed loop high-throughput experimentation environment.
• Disseminate scientific results to the broader community by publishing impactful research papers in reputed conferences and journals.
• Collaborate with PCL nodes jointly under the PCL research network.
• Supervise graduate research assistants, undergraduates and the workforce originating from the AI-BIOME PCL.
• Work alongside our expansive network of partners, including researchers at Brookhaven National Laboratory, NVIDIA, Mozilla AI and other collaborators in major National Labs and companies.
• Other duties as assigned.

Benefits

• RFCUNY Employee Benefits and Accruals

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree