Research Fellow (m/f/x) in AI for Autonomous Scientific Discovery and Scientific Machine Learning
Core
Develop scientific machine learning and AI-microscopy integration methods to analyze experimental data streams and actively guide experiments through feedback.
Role type
Research Fellow (IC)
Builds
Reinforcement learning frameworks for autonomous experimental control and pattern recognition models for microscopy data
Domain
Physics (soft condensed matter, nonequilibrium, nonlinear physics) + AI/ML
Deliverable
production ML models
Required skills
Reinforcement learning, Python, PyTorch/TensorFlow, scientific computing, pattern recognition, large-scale data analysis
Preferred skills
Actor-critic algorithms, multimodal imaging integration, quantitative measures of order/disorder (via careerplan.io/jobs/10001-1003813349-S-research-fellow-mfx-in-ai-for-autonomous-scientific-discovery-and-scientific-machine-learn)
Technologies
PyTorch, TensorFlow, Python
Responsibilities
Develop actor-critic and reinforcement learning algorithms for experimental control; Design reward functions for pattern optimization and rare-event discovery; Develop supervised and unsupervised learning approaches for pattern classification; Analyze large-scale microscopy datasets; Publish findings in leading journals and conferences
