Postdoctoral Research Associate – Autonomous Science & Robotic AI
Bridge computational AI architectures with physical lab automation in an interdisciplinary postdoctoral role at Boston University.
Best for:Best for interdisciplinary researchers with expertise in both machine learning architectures and physical laboratory automation or robotics.
Overview
Boston University seeks an ambitious Postdoctoral Research Associate to act as an intellectual and technical bridge between its AI Group and Automation Group. The role involves translating complex computational concepts into physical lab execution protocols and integrating LLM agents and reinforcement learning with automated robotic systems. The successful candidate will drive closed-loop system integration, mentor graduate students, and author high-impact scientific publications. The appointment is a full-time, one-year initial position with opportunities for renewal.
What you'll do
- Translate complex computational models and reinforcement learning policies into automated physical laboratory protocols.
- Map polymer science workflows into structured representations suitable for AI planning and optimization algorithms.
- Lead, mentor, and coordinate a multidisciplinary team of graduate students across computer science, robotics, and materials science.
- Collaborate with the AI team to deploy instruction-tuned LLM agents and reinforcement learning algorithms for hardware control.
- Integrate software APIs and ROS interfaces with automated liquid handlers and polymer processing equipment.
- Author high-impact scientific publications in top-tier journals and conferences while contributing to grant proposals.
Requirements
Degree required: 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.
Language: English
Instruction: English
Publications: Track record or demonstrated capability to draft high-impact scientific publications in top-tier journals and conferences.
- Demonstrated programming capabilities in Python including PyTorch and the standard data science stack.
- Experience with Large Language Model agent frameworks and fine-tuning or instruction tuning methods.
- Understanding of reinforcement learning principles, multi-objective optimization, or active learning.
- Familiarity with lab automation systems, automated liquid handling, or microfluidics platforms.
- Knowledge of hardware control interfaces such as APIs, serial communications, or ROS/ROS2.
- Strong growth mindset and willingness to bridge technical knowledge gaps between AI software and physical hardware.
- Must hold a completed Ph.D. in a relevant quantitative scientific or engineering discipline prior to starting.
Funding & terms
How to apply
Submit your curriculum vitae and a cover letter containing the names of three references through the Academic Jobs Online portal.
- CV
- Cover letter
- 3 reference letters
Contact
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