99 computer-science-programming-languages-"Prof"-"TU-Dresden" PhD scholarships in Germany
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Application Deadline 15 Jan 2026 - 23:00 (UTC) Type of Contract To be defined Job Status Part-time Hours Per Week To be defined Is the job funded through the EU Research Framework Programme? Not funded by a EU
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program in and beyond the fields mentioned above Competitive, interdisciplinary environment with a track record of intense mutual collaboration Individual training-through-research, program extends
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increasing atmospheric pollution by space emissions. On the regional level, we closely cooperate with the University of Rostock and are an integral part of the teaching program of the Institute of Physics
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physics, materials science, computer science or a related discipline background in one or more of the following areas is desired: X-ray or electron imaging techniques, and image processing proficiency in
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Ingolstadt. The salary is prescribed by the framework of the collective agreement (TV-L), Level 13 (75%). The research group Reliable Machine Learning (headed by Prof. Felix Voigtlaender) is part of
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background in bioinformatics, systems biology, molecular biology or a related field You should have strong programming skills (preferably in R and Python) and familiarity with Linux Experience in constraint
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Requirements: Applicants should hold an MSc or Diploma in Engineering, Computer Science or a related discipline. Background in Machine Learning and Artificial Intelligence. Strong programming skills (Python
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independent work and effective collaboration, particularly during measurement campaigns at synchrotron and neutron facilities. Experience with programming languages such as Python is advantageous. Participation
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. The communication language of the lab is English. The group interacts tightly with the Research Group Cognitive Neurophysiology (PI M. Yoshida; in-vitro/in vivo electrophysiology & computational neurosciences) as
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practical lab position, so applicants with only (bio-)informatics background will not be considered. For inquiries, please contact: Prof. Thomas Weide ( weidet@uni-muenster.de ; Thomas.Weide@ukmuenster.de