Postdoctoral Research Associate – Autonomous Science & Robotic AI Orchestration

About this position

Postdoctoral Research Associate, Boston University, Division of Materials Science & Engineering and Division of Systems Engineering

Position Type: Full-Time, 1-Year Initial Appointment (Renewable)

Position Overview:

We are seeking an ambitious, cross-disciplinary Postdoctoral Research Associate to serve as the critical intellectual and technical bridge between two cutting-edge research teams:

• The AI Group: Experts in systems, control, optimization, Large Language Model (LLM) training, domain adaptation, and instruction tuning, agentic AI orchestration, and reinforcement learning (RL).

• The Automation Group: Experts in designing and deploying custom robotic systems engineered for high-throughput sample synthesis, processing, and analytical characterization.

This role is ideal for a researcher who possesses working fluency across both computational AI architectures and physical lab automation. We are seeking someone with an eagerness to master missing domains and a strong enthusiasm to lead and mentor a collaborative group of graduate students working at the intersection of self-driving laboratories and systems biology.

Key Duties & Responsibilities

• Interdisciplinary Bridge & Translation:
• Translate complex computational concepts (agent reasoning, instruction tuning, offline/online RL policies) into physical, automated lab execution protocols.
• Map domain-specific polymer science workflows into structured representations suitable for AI planning and optimization algorithms.

• Research Leadership & Mentorship:
• Abstract from the practical challenges presented by the work and formulate general research problems with wider applicability, leading to impactful methodological contributions.
• Lead, mentor, and coordinate a team of Ph.D. and Master’s graduate students across computer science, robotics, and materials science disciplines.
• Design collaborative milestone roadmaps and ensure seamless execution of joint computational-experimental deliverables.

• Closed-Loop System Integration:
• Collaborate with the AI team to implement domain-adapted, instruction-tuned LLM agents and RL algorithms that dynamically steer hardware execution.
• Work alongside robotics engineers to integrate software interfaces (APIs/ROS) with automated liquid handlers, analytical instruments, and polymer processing modules.

• Publishing & Dissemination:
• Draft high-impact scientific publications in top-tier interdisciplinary and domain-specific journals/conferences (e.g., Science Robotics, Nature Communications, NeurIPS/ICML/ICLR/AAAI/AISTATS/ICRA/IROS, ACS Materials/Polymers, and various IEEE Transactions).
• Present research findings at major scientific conferences and contribute to grant proposals for funding agencies.

Desired Skills & Experience

• Core Requirements:
• Education: Ph.D. in Computer Science, Robotics, Systems Engineering, Electrical and Computer Engineering, Mechanical Engineering, Chemical Engineering, Materials Science & Engineering, Chemistry, or a related quantitative scientific discipline.
• Growth Mindset & Curiosity: Broad technical curiosity with a willingness to rapidly learn and bridge technical knowledge gaps (whether on the AI software side or the physical robotics/polymer side).
• Mentorship & Leadership: Demonstrated capacity or strong desire to guide, mentor, and foster an inclusive research environment for graduate students.

• Technical Skills (Combination across AI & Hardware/Systems biology domain):
• AI & Computational Side:
• Experience or familiarity with LLM/Agent frameworks (e.g., LangChain/LangGraph, AutoGen, CrewAI, Hugging face ecosystem, MCP) and fine-tuning/instruction tuning methods.
• Understanding of Reinforcement Learning (RL) principles, multi-objective optimization, or active learning/Bayesian optimization.
• Strong programming capabilities in Python (PyTorch, standard data science stack).

• Robotics & Systems Biology Side:
• Experience or familiarity with lab automation systems, automated liquid handling, or microfluidics/sample prep platforms.
• Familiarity with hardware control interfaces (APIs, serial communications, ROS/ROS2, microservices architecture).
• Understanding of basic chemistry/characterization (e.g., viscosity, GPC/SEC, FTIR, mechanical testing) or automated materials screening workflows.

To apply, please submit your CV and a cover letter that includes the names of three references to Academic Jobs on Line. Applications will be reviewed on a rolling basis until the position is filled.

For more information, please go to www.bu.edu/mse or www.bu.edu/se.

Boston University is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Boston University is also a VEVRAA Federal Contractor.