At the earliest possible date. We are offering a fixed-term part-time position (75%), initially limited to three years. The CRC 1459 and its integrated research training group offer an outstanding infrastructure and a comprehensive and interdisciplinary training program in the design and study of intelligent matter. The program will enable the researcher to carry out frontier research and empower them for future research and management tasks in industry and academia. Full-time employees are required to teach 4 hours per week during the semester.
The researcher position forms part of an intensive and interdisciplinary research program on the conceptualization and realization of intelligent matter. Intelligence is the ability to perceive information and to retain it as knowledge to be applied towards adaptive behaviour in a changing environment. The CRC 1459 is inspired by the question whether synthetic matter can have intelligent properties. Such intelligent matter would provide entirely new opportunities for instance for the development of artificial skin, soft robotics with adaptive tactility, and brain-inspired computing with reduced energy consumption. The CRC 1459 involves 26 project leaders at University of Münster and University of Twente. More information on the research program of CRC 1459 is available at:. This project is project C02 in the group of Professors Pernice and Van der Wiel.
Your tasks:
* Experimental research in project C02 in the CRC 1459
* Collaboration with other research groups within the CRC 1459
* participation in self-administration
* collaboration in research and teaching
* This position is tied to working towards a doctorate (position at least 75%)
Our expectations:
* Outstanding degree in physics and/or computer science (i.e. German Diplom/or master´s degree)
* Experience in efficient machine learning for low-power and resource-constrained hardware. Ability to develop hardware-aware ML pipelines, including data preparation and model optimization.
* Expertise in temporal signal processing and time-dependent learning tasks.
* Familiarity with neuromorphic or unconventional computing hardware (e.g. RNPU-like systems).
* Experience with multimodal sensing and sensor-data fusion.
* Strong background in scalable data processing and benchmarking for ML tasks.
* Experience with low-latency ML models for real-time signal processing.
* Ability to interface machine learning with experimental hardware platforms.
* Proficiency in Python/C++ and modern ML frameworks (e.g. PyTorch) in Linux environments.
* Interdisciplinary background in machine learning, electronics, and dynamical systems.
Advantages for you:
* A vibrant, interdisciplinary environment with outstanding facilities and a track record of intense and successful scientific collaboration.
* A stimulating environment for professional success including lectures, colloquia, workshops and symposia in addition to individual training trough research.
* Appreciation, commitment, openness and respect – values which are important to us.
* If you have family members or young children in your care, our Family Service Office offers concrete support to help you balance your private and professional responsibilities.
* As an educational institution, we are deeply committed to offering occupational training and continuing education opportunities tailored to your individual needs.
* As a university employee, you are entitled to numerous benefits afforded to public servants, e.g. an attractive company pension scheme ( VBL ), an annual end-of-year bonus and a position that is shielded from economic fluctuations.
The University of Münster strongly supports equal opportunity and diversity. We welcome all applicants regardless of sex, nationality, ethnic or social background, religion or worldview, disability, age, sexual orientation or gender identity. We are committed to creating family-friendly working conditions.
We actively encourage applications by women. Women with equivalent qualifications and academic achievements will be preferentially considered unless these are outweighed by reasons which necessitate the selection of another candidate.
Are you interested? Then we look forward to receiving your complete CV, a recommendation letter, and a motivation letter (maximum 1 page) by 2026-05-01. Please send your application electronically in PDF format to offper@uni-muenster.de with reference number PhD2026. Please note that we cannot consider other file formats. Reference number: 2026_04_05