Master Thesis – Reinforcement Learning Control of a Liquid–Liquid Separator Using Physics-Informed Neural Networks - Inklusiver Job

Place of Employment: Jülich Start Date: To the next possible date Salary: We will pay you a appropriate remunerationfor your thesis Application Deadline: 2026-11-01 Index number: 2026M-0795 Your Job Develop a reinforcement learning (RL) controller for a liquid–liquid gravity settler, trained entirely offline in a simulated environment Use existing physics-informed neural network (PINN) models of the separator dynamics as fast virtual training environments Formulate the control problem and desig…