Post-doc in Learning in Biological and Soft Matter

Technical University of Munich; München; Vor Ort; EUR 45.000 - 55.000; Vollzeit

Zusammenfassung

A leading research institution in Munich is seeking a Post-doc in Learning in Biological and Soft Matter. The candidate will investigate learning mechanisms in Physarum and develop theories on soft matter learning. A doctoral degree in physics or applied mathematics is required, along with knowledge in complex systems physics. This position offers a flexible contract with a collaborative and diverse research team starting as early as April 2026.

Qualifikationen

• Completed a doctoral thesis in a relevant discipline.
• Enjoy working in interdisciplinary and international teams.
• Express yourself confidently in English.

Aufgaben

• Investigate the emergence of learning in a network-shaped single cell (Physarum).
• Develop a theory describing the physical mechanisms of Physarum’s learning processes.
• Predict implementations of learning in soft matter.

Kenntnisse

Knowledge in soft matter/complex systems physics

Programming skills

Strong communication in English (oral and written)

Ausbildung

Doctoral thesis in physics, applied mathematics or related disciplines

Jobbeschreibung

Post-doc in Learning in Biological and Soft Matter

Prof. Karen Alim’s group on Biological Physics and Morphogenesis at the TUM Campus Garching is looking for a Post‑doc (m/f/d) to join our team on the ERC project Learning Matters!.

Responsibilities

You will break with the current focus on the brain to uncover the physics of continual learning instead by investigating the emergence of learning bottom‑up in life, reduced in complexity to a network‑shaped single cell – Physarum. Lacking any neurons, flows flushing throughout Physarum’s tubular network propagate input packaged as chemical concentration and flow shear force. The tube wall’s viscoelasticity reorganises in response – continually learning its future response. You will develop a theory describing the physical mechanisms of Physarum’s learning mechanics and thereby predict how to implement learning in soft matter, as Physarum’s responsiveness in wall visco‑elasticity has its soft matter twin in synthetic hydrogels.

Qualifications

As a suitable candidate, you have completed a doctoral thesis in physics, applied mathematics or related disciplines. You have knowledge in soft matter/complex systems physics or statistical physics. You enjoy working in interdisciplinary and international teams and have programming skills. In addition, you are able to express yourself confidently both orally and in writing in English.

Offer

We offer a two‑plus‑two‑year contract (TV‑L E13) with a flexible start date as early as April 2026 in a highly motivated team combining experimental and theoretical research on equal footing. As an equal opportunity and affirmative action employer, TUM explicitly encourages applications from women as well as from all others who would bring additional diversity dimensions to the university’s research and teaching strategies. The position is suitable for individuals with disabilities. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.

Application

We are looking forward to receiving your application documents, which include your CV, your list of publications, transcript of record, a motivation of your research interests (max. 1 page) and the contact details for two letters of recommendation in one PDF document. Please send these under the subject "Post‑doc Learning in Biological and Soft Matter" by e‑mail to Prof. Dr. Karen Alim ([e-mail]) by 15.02.2026. She will also be happy to provide you with further information in advance.

Personal Data

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Website

www.bpm.ph.tum.de