PhD Candidate in Reinforcement Learning for Planning Problems | Doktorand*in im Bereich Reinforcement Learning für Planungsprobleme (26089)

Job Summary

This role at the University of Wuppertal offers a unique opportunity to conduct cutting-edge research at the intersection of Machine Learning and combinatorial optimization. As a PhD Candidate within the Institute for Technologies and Management of Digital Transformation (TMDT), you will focus on Neural Combinatorial Optimization, developing and evaluating innovative Reinforcement Learning models to solve complex industrial planning and logistics challenges. Your daily activities will involve scientific research, training deep learning models, and publishing findings at prestigious international conferences like AAAI or KDD. This position is ideal for researchers who want to bridge the gap between fundamental AI theory and practical industrial application. The university provides a supportive, international environment with flexible working hours, home office options, and a clear path toward completing a doctoral degree within a three-year initial contract.