Job Description

Position: Post-doctoral Position

Deadline: to be advertised soon (approx. 15.05.2026, although earlier applications are preferred)

Employment Start Date: Between 15.07 and 15.09.2026 (to be discussed)

Contract Length: 24 months

Description:

The Computational Neurology group at Ruhr University Bochum (RUB), led by Prof. Dr. Xenia Kobeleva , investigates clinically relevant questions inneuropsychiatry and neurodegenerative diseases using advancedcomputational approaches, including machine learning, artificial intelligence, and brain modeling. The group is situated in the Institute for Neural Computation and is part of the Bernstein Node Bochum of the Science Hub Neuroscience. Our group works at the interface of clinical neuroscience (neurostimulation and neurodegenerative diseases) and computational neuroscience (whole-brain network neural modeling, parameter inference, control theory), with a strong focus on translational research that connects computational methods to real-world clinical data and patient-oriented questions. Our team is young, motivated, international, and genuinely interdisciplinary, bringing together expertise from medicine, neuroscience, psychology, and quantitative sciences. Besides a direct connection to clinical neurology, we are intensively collaborating with world-leading experts for mathematics and computer science.

Your responsibilities

• Developing hybrid NeuroAI models at the interface of ANN and differential-equation-based models.
• Presenting results in national and international conference and publish in peer-reviewed journals.
• Supervision of junior team members.
• Writing of grant proposals.
• Minor teaching obligations (2 hours/week).

What we offer

• An inspiring research environment with strong clinical and interdisciplinary connections at the Institute for Neural Computation and Science Hub Neuroscience, fostering independent scientific thinking while providing continuous academic and career support, providing regular seminars with other members of the institute and a yearly retreat.
• Employment according to German public sector standards (TV-L), including social security benefits, paid leave, and a stable framework that enables focused, high-quality doctoral research.
• A family-friendly working environment that actively supports the reconciliation of academic work and family life, including flexible working arrangements and institutional support structures in line with RUB’s commitment to equal opportunity and diversity. We actively work against scientific misconduct by fostering an open environment and a focus on reproducible science.

Your profile

• You are genuinely curious and interested in exploring how the brain works and what can be done to fix it in case of disorders. You can solve problems in a creative way and like to define research gaps by looking across applied and theoretical research. You have experience in interaction both with experimental and theoretical scientists, evidenced by interdisciplinary publications.
• You have a PhD with a quantitative background (such as computational neuroscience, computer science, physcis, mathematics, biomedical engineering, or similar) with prior experience in neural networks (either based on PDE or ANN or ideally both), and you are expert in Python programming.
• Ideally, you are experienced in dynamical systems, and preprocessing and analyzing macroscale neuroimaging data.
• You have a strong interest in neuroscientific methods as well as in elaborating potential diagnostic and treatment options for neuropsychiatric disorders.
• You demonstrate good communication, organizational and teamwork skills.

Application
Please send your application documents (including cover letter, a CV, list of publications, and contact emails of two references) as a single PDF file to Prof. Dr. Xenia Kobeleva (xenia.kobeleva@rub.de). Applications will be reviewed until the position has been filled and earlier applications will be preferred.

Ruhr University Bochum is committed to equal opportunity in employment and gender equality in its working environment. To increase gender distribution in all job categories and at all levels, we strongly encourage applications from qualified women. Female applicants will be given preferential consideration when their level of qualification, competence and professional achievements equals that of male candidates, unless arguments based on the personal background of a male co-applicant prevail. Applications from appropriately qualified handicapped persons are also encouraged.