PhD Position in Numerical Simulations and Machine Learning for Active Matter Systems Forschungszentrum Jülich in Germany

The PhD project focuses on the study of active matter systems, which are composed of individual units that consume energy to generate motion or mechanical forces, resulting in collective organization and behavior. Active matter is a rapidly growing field in physics, with examples ranging from bacterial colonies and cytoskeletons driven by molecular motors to motile cells in tissues and self-propelled particles. These systems are inherently out of equilibrium due to continuous energy injection at the particle or agent level.

The research aims to combine numerical simulations and machine learning to better understand and characterize active matter systems. One direction involves using physics-informed machine learning techniques to connect mesoscopic properties of active units within a collective to their macroscopic behavior, based on simulation data. Another approach is to equip active units with learning capabilities or adaptive interactions to steer the emergent behavior of the collective.

The core of the PhD project will be numerical simulations of active systems, with machine learning serving as a tool to analyze and augment their behavior in a controlled manner. Specific tasks include running numerical simulations, applying physics-informed machine learning techniques, improving and developing new models, participating in conferences in Germany and abroad (including presenting research results), and preparing scientific publications and project reports.

Applicants should have a strong background in physics, mathematics, or computer science, and experience with numerical simulations and machine learning is highly desirable. The position offers opportunities to engage in cutting-edge research, develop new models, and present findings at international conferences. The institution is Forschungszentrum Jülich, a leading research center in Germany.

To apply, candidates should use the online application portal and submit their CV, motivation letter, and relevant documents. The application link is provided above. For further information, applicants may contact the institution directly.