PhD Position – Active Matter and Machine Learning Full-time

PhD Position – Active Matter and Machine Learning

In the Institute for Advanced Simulation – Theoretical Physics of Living Matter (IAS-2), we employ theory, modeling, and simulation to study and understand the structure, self-assembly, and dynamics of complex fluids, active soft matter, and living matter – on the basis of physical principles. We focus on non-equilibrium systems, where internal activity and/or external driving forces maintain the system away from thermal equilibrium, leading to complex structure formation, dynamics, and flow behavior. Examples include collective behavior of self-propelled objects, the flow behavior of biofluids, cellular and tissue organization.

The field of active matter is represented by systems made up of individual units that consume energy to generate motion or mechanical forces, causing the system to organize and behave collectively. The energy is continuously injected at the level of the individual particles or agents, keeping the system out of equilibrium. Examples of active matter include bacterial colonies, cytoskeleton formed by filaments and driven by molecular motors, motile cells in a tissue, self-propelled particles and their collectives. Even though the complexity and diverse properties of active systems make their investigation very challenging, active matter research presents great opportunities for finding novel physical mechanisms and for using such systems in possible applications.

In this PhD project, we will combine numerical simulations and machine learning in order to better understand and characterize active matter systems. A possible direction is to use physics-informed machine learning techniques to connect mesoscopic properties of active units within a collective to its macroscopic behavior based on simulation data. Another possibility is to equip active units with learning capabilities or adaptive interactions in order to steer the emergent behavior of a collective. The base of the PhD project will be numerical simulations of active systems, where machine learning will be used as a tool to better understand them or augment their behavior in a controlled way.

Specifically, your tasks will be:

• Running numerical simulations of active systems
• Using physics-informed machine learning techniques to characterize their behavior
• Improving and developing new models
• Participating in conferences in Germany and abroad (incl. presenting your research results)
• Preparing scientific publications and project reports

Your profile should include:

• A master's degree or diploma in physics, applied mathematics, or a relevant engineering discipline
• Strong motivation for an interdisciplinary project that combines numerical models, simulations, and machine learning
• Good programming skills and experience with numerical modeling
• High degree of independence and commitment
• Very good command of written and spoken English with extensive vocabulary is required (at least B2 level according to the CEFR), ideally supported by a certificate confirming the language level
• Experience with machine learning and high-performance computing is advantageous, but not necessary

Our benefits for you include:

• Research & Infrastructure: You will participate in a world-leading international research environment with cutting-edge computational facilities including the on-site top European supercomputers
• Team & Environment: You will work in an experienced and friendly international research team with a strong background in biophysics and active matter
• Networking & Exchange: You will have the opportunity to participate in (international) conferences and project meetings
• Supervision & Support: We will accompany your doctoral studies with continuous, expert guidance from your scientific supervisors
• Work-life balance: We offer flexible working hours to help you balance your professional and personal life. You also have the option of flexible working (in terms of location), which is generally possible after consultation and in line with upcoming tasks and (on-site) appointments
• Annual Leave: You get 30 days of annual leave
• Knowledge & Development: Your professional development is important to us – we support you specifically and individually e.g., through training and networking opportunities specifically for doctoral candidates (JuDocS)
• Fair remuneration: Depending on your qualifications and assigned responsibilities, you will be classified according to pay group 13 (75%) of the TVöD-Bund. In addition to the basic salary, there is an additional year-end bonus under the collective pay agreement amounting to 75% of a monthly salary, as well as capital-forming benefits. All information about the TVöD-Bund collective agreement can be found on the BMI website (pay scale table on page 66 ff. of the PDF download)
• Fixed-term: The position is initially limited to 3 years

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.