Now Accepting Applications for PhD Positions in Safe Reinforcement Learning at Rochester Institute of Technology
Apply now for fully funded PhD positions in Safe Reinforcement Learning at the Rochester Institute of Technology. Develop theoretical foundations and algorithmic frameworks at the intersection of formal methods, control theory, and machine learning.
We are currently seeking 1-2 candidates for fully funded PhD positions in reinforcement learning at the Rochester Institute of Technology. The focus of these positions will be on developing theoretical foundations and algorithmic frameworks at the intersection of formal methods, control theory, and reinforcement learning. Successful candidates will investigate fundamental questions in safe reinforcement learning, including the development of verifiable safety guarantees for learning-based control systems, integration of formal methods with reinforcement learning algorithms, theoretical frameworks for constraint satisfaction in sequential decision-making, and approaches to safety specification and verification in autonomous systems.
Required qualifications include a Master’s degree in Computer Science, Mathematics, or a related field, a strong mathematical background in optimization, control theory, and machine learning, and programming experience in Python and common ML frameworks. To apply, please send your CV and contact information for 2 academic references to akbeme@rit.edu.
Tags: PhD positions, Safe Reinforcement Learning, Rochester Institute of Technology, Formal Methods, Control Theory, Machine Learning, Python, ML frameworks