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Forschungszentrum Jülich

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Master Thesis in Reinforcement Learning Control of a Liquid–Liquid Separator Using Physics-Informed Neural Networks Forschungszentrum Jülich GmbH in Germany

Degree Level

Master's

Field of study

Computer Science

Funding

Paid master thesis with appropriate remuneration. The post also mentions flexible working hours and possible flexible working location after consultation.

Deadline

Nov 1, 2026

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Country

Germany

University

Forschungszentrum Jülich

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Keywords

Computer Science
Mechanical Engineering
Electrical Engineering
Chemical Engineering
Mathematics
Process Engineering
Reinforcement Learning
Monte Carlo Simulation
ML

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About this position

Master Thesis Opportunity at Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems - Energy Systems Engineering (ICE-1), in Jülich, Germany.

The project focuses on reinforcement learning, physics-informed neural networks, process control, and liquid-liquid separation. You will develop an offline RL controller for a liquid-liquid gravity settler, use existing PINN models as fast virtual training environments, design reward functions for throughput and separation quality, handle process constraints during learning, and evaluate the controller on a more detailed process model. Validated surrogate models and experimental pilot-scale data are available.

Eligibility highlights: current master's student in Process Systems Engineering, Computational Engineering Science, Chemical Engineering, or a comparable program; Python programming required; PyTorch experience is a plus; experience in modeling, simulation, and machine learning is expected; basic control engineering knowledge is needed; RL experience is beneficial but not required; English at least B2 CEFR level, ideally with a certificate.

Funding: paid thesis with appropriate remuneration.

Deadline: 2026-11-01. Start date: as soon as possible.

How to apply: submit an online application through the job posting/career portal. Use the application information link for instructions. For questions, contact [email protected].

Funding details

Paid master thesis with appropriate remuneration. The post also mentions flexible working hours and possible flexible working location after consultation.

What's required

Current master's student in Process Systems Engineering, Computational Engineering Science, Chemical Engineering, or a comparable program. Programming experience in Python is required; PyTorch experience is a plus. Experience in modeling, simulation, and machine learning is expected, along with basic knowledge of control engineering. Reinforcement learning experience is not required but is highly beneficial. Very good written and spoken English at least B2 CEFR level is required, ideally supported by a language certificate. Applicants should be independent, motivated, reliable, analytical, and able to work well in a team.

How to apply

Apply online via the career portal/job posting. Use the application information link for instructions and the career portal for submission. For questions, use the provided contact form or email contact.

More information can be found here

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