One part-time Researcher for machine learning within CytoTransport research at Hochschule BonnRheinSieg HBRS
Sankt Augustin, , Germany -
Full Time


Start Date

Immediate

Expiry Date

03 Dec, 25

Salary

0.0

Posted On

03 Sep, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

Discovering new things, driving projects forward, taking responsibility: At Bonn-Rhein-Sieg University of Applied Sciences (H-BRS), we count on employees who do just that. Together with our approximately 1,000 colleagues, we find new solutions to help find suitable answers to the diverse challenges of our time. We do this so that H-BRS, with its more than 9,000 students from more than 100 nations who are currently studying on 40 degree programs, can become even better. Do you accept the challenge?
The German Research Foundation (DFG) is funding our research cluster “CytoTransport - Mechanisms and Modulation of Cellular Transport Processes” (Research Impulse 26/1). The 5 years period started on April 1st, 2024. Our project combines expertise in biomedicine, computational modeling, structural biology, chemistry and materials science for investigating cellular transport mechanisms in human health and diseases. Nine research groups in the two research institutes IFGA (Institute for Functional Gene Analytics) and TREE (Institute for Technology, Resource and Energy-efficient Engineering) are working together towards that goal. Detailed information on the research cluster’s objectives and the individual projects can be found at: https://www.h-brs.de/en/cytotransport.
We are looking for the following employee (d/f/m) to join our “CytoTransport” team as soon as possible in our Department of Computer Science on Campus Sankt Augustin:

How To Apply:

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Responsibilities
  • will contribute to research and development work in the DFG-funded research cluster “CytoTransport – Mechanisms and Modulation of Cellular Transport Processes.”
  • will develop and implement machine learning models to support protein modeling and generate plausible conformational ensembles for molecules.
  • will implement existing machine learning models for tasks such as homology modeling, small-molecule docking, or similar problems.
  • will be responsible for evaluating and presenting research results at national and international conferences, and for publishing them in scientific journals.
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