91 programming-"the"-"DAAD"-"U"-"Prof"-"IMPRS-ML"-"O.P"-"UCL" positions at University of Vienna
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at the department This is part of your personality: Completed Master's degree or Diploma, programme in natural sciences or equivalent aptitude. Didactic competences / experience with e-learning Excellent command
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areas. Your future tasks: Successful candidates will actively participate in the training of archivists, further develop the training program in consultation with the archives, coordinate external
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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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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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experienced programmer, preferably using the Python programming language. Experience modeling and solving vehicle routing problems involving variable travel durations is a plus. Experience modeling and solving
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from you: Applicants should have completed their Master/Diploma degrees (or be very near completion) by the time of their application. We are looking for graduates from: Renowned management programs: e.g
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• Experience in organizing conferences and workshops • Experience in media analysis, working with databases, literature management systems, and AI programs • Experience in public history communication and media
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join our accomplished team! Your personal sphere of influence: The Doctoral School Computer Science DoCS (https://docs.univie.ac.at) founded in 2020 offers a structured doctoral programme that supports
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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