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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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programme, please see https://uni-tuebingen.de/en/faculties/faculty-of-science/doctoral-studies/ and https://www.phd.tuebingen.mpg.de/imprs? Assessment Submitted applications will be reviewed by IMPRS
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(UTC) Type of Contract To be defined Job Status Other Offer Starting Date 13 Oct 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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on conferences and in publications Requirements Master’s degree in physics or chemistry, computer science or equivalent Interest in Physics and Machine Learning Good written and spoken English Ability to work both
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electrical engineering, computational engineering, or a related discipline A strong foundation in power system modelling and simulation Solid programming skills (Python, C++, or comparable languages) Interest
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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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13 Nov 2025 Job Information Organisation/Company Universität Siegen Research Field Computer science » Informatics Researcher Profile First Stage Researcher (R1) Country Germany Application Deadline
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Chemistry » Physical chemistry Computer science » Programming Computer science » Systems design Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Germany Application Deadline 28
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international research contexts Your Profile: Excellent Master’s degree in mechanical engineering, energy systems, computational engineering, or a related field Strong background in numerical methods and applied
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(UTC) Type of Contract To be defined Job Status Other Offer Starting Date 13 Oct 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to