105 programming-"the"-"DAAD"-"IMPRS-ML"-"FEMTO-ST"-"UCL"-"Prof"-"https:" positions in Austria
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initially limited until 17.11.2025 and can be extended for the duration of the employee´s absence. Your future tasks: Active participation in research, teaching & administration, which means: You plan and
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of research interests / ideas for a prospective doctoral project proposal With a plan for the completion of the doctorate With a confirmation of enrolment in the doctoral programme/having passed the FÖP Via our
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. This is part of your personality: Master’s degree in Data Science, Computational Biology, Bioinformatics, Chemical Engineering, Systems Biology, or a related field. Good programming skills in Python, R
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17.11.2025 and can be extended for the duration of the employee´s absence. Your future tasks: Active participation in research, teaching & administration, which means: You plan and conduct your own research
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the opportunities for cooperation with the faculty’s key research areas. Your future tasks: Successful candidates will actively participate in the training of archivists, further develop the training program in
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in translation and interpreting, and 2) translation and interpreting in social, institutional and media contexts. The Centre offers the following study programmes: Bachelor’s programme “Transcultural
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, Bioinformatics, Chemical Engineering, Systems Biology, or a related field. Good programming skills in Python, R, or similar languages. Experience with mathematical modeling, process optimization, or AI methods is
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scientific curriculum vitae / letter of intent With your summary of research interests / ideas for a prospective doctoral project proposal With a plan for the completion of the doctorate With a confirmation of
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organise scientific events. You prepare and complete a publication-ready habilitation. You hold courses independently in the Bachelor programme „Languages and Cultures of South Asia and Tibet” and the Master
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to the department Ph.D. program and will work on the development and analysis of statistical methods for machine learning, particularly in the context of high-dimensional models and with a particular focus on methods