Postdoctoral Research Fellow – AI Self-Driven Laboratory for Biology

The Singapore University of Technology and Design (SUTD) invites applications for a Postdoctoral Research Fellow position in the area of AI-driven experimental systems for biology. This position is hosted within the Engineering Product Development (EPD) pillar and is part of a strategic research effort to develop next-generation AI self-driven laboratories for biological discovery.

EPD is a multidisciplinary pillar at SUTD that addresses complex technological challenges through a design-centric and systems-oriented approach. The successful candidate will contribute to an interdisciplinary research programme at the intersection of optics, electrical engineering, artificial intelligence, and experimental biology, with the aim of building closed-loop, autonomous experimental platforms that accelerate scientific discovery.

The successful candidate is expected to:

• Design and develop optical sensing and imaging systems for biological experiments (e.g. microscopy, spectroscopy, fluorescence-based readouts).
• Develop electronic and hardware control systems for laboratory instrumentation, including sensors, actuators, microfluidic devices, and automation components.
• Integrate experimental hardware with software and data pipelines to enable real-time data acquisition and closed-loop control.
• Collaborate with AI researchers to implement machine learning models for adaptive experimental design and autonomous decision-making.
• Build robust, scalable experimental platforms capable of long-duration and autonomous operation.
• Contribute to high-impact journal and conference publications
• Mentor graduate students and contribute to a collaborative, interdisciplinary research culture.

The candidate must have:

• • A PhD in Electrical Engineering, Applied Physics, Robotics, or a closely related discipline.
  • Strong background in optical systems and experimental instrumentation.
  • Demonstrated experience with electronics, sensors, and data acquisition systems.
  • Proficiency in programming
  • Proven ability to design, build, and debug real-world experimental systems.

Candidates meeting the following criteria will be favorably evaluated:

• • Prior exposure to biological, chemical, or microfluidic experimental platforms.
  • Experience integrating machine learning or data-driven models with physical systems.
  • Experience with laboratory automation, robotics, or autonomous experimentation.
  • Ability to work effectively across disciplinary boundaries and communicate with domain scientists.

Interested candidates should submit the following documents:

• • Cover letter
  • Curriculum Vitae
  • Brief statement (1–2 pages) describing relevant technical experience and systems built
  • Names and contact details of two referees