Assistant Scientist – AI for Autonomous Synthesis and Multimodal Characterization
Core
Developing and applying AI/ML methods for autonomous, self-driving synthesis of nanoscale and quantum materials using closed-loop experimental workflows.
Role type
Research Scientist (AI for Autonomous Materials Synthesis)
Builds
Closed-loop AI-enabled experimental workflows integrating synthesis with in situ/operando x-ray, electron, and optical characterization.
Domain
Materials Science / Quantum Materials / Synchrotron Science / Machine Learning
Deliverable
production ML models
Required skills
Active learning, Bayesian optimization, Generative models, Inverse design, Deep learning frameworks (PyTorch, TensorFlow, JAX), Optimization libraries (BoTorch, GPyTorch, scikit-learn), Python programming, Multimodal data analysis, HPC integration
Preferred skills
Experimental control frameworks (ROS, Bluesky, EPICS), Robotic synthesis platforms, Reinforcement learning, Agentic AI, Multimodal data fusion, Digital twins, Physics-informed machine learning
Responsibilities
Lead research program in AI-enabled autonomous materials synthesis, Design closed-loop experimental workflows, Develop AI/ML methods for active learning and experiment planning, Build analysis tools for multimodal high-throughput data, Collaborate with synthesis and characterization scientists, Publish in peer-reviewed journals
Seniority
Senior, hands-on IC