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University of Vienna, the PhD program for life scientists and computational scientists/machine learning experts will start in January 2026. The goal of the PhD Program is to address real-world problems in
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Michael Bronstein, AITHYRA Scientific Director AI and Honorary Professor of the Technical University of Vienna in collaboration with Ismail Ilkan Ceylan, expert in graph machine learning, invites
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outstanding candidates whose work lies at the intersection of statistics, machine learning, data analytics and modern AI algorithms. This includes, in particular, statistics for high-dimensional and complex
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spoken German/ willingness to learn German Computer skills: MATLAB and/or R desirable You are motivated and self-propelled You are flexible and creative You should be a team player with high social skills
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of the candidate), in visualization and data analysis, cooperative systems, data mining and machine learning, education, didactics and entertainment computing, or Neuroinformatics. Across faculties, renowned
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Tenure-Track Professor in the field of. Sociology with focus on Quantitative Social Science Research
quantitative empirical social research in sociology such as causal inference or machine learning or complex panel data analysis. We are seeking excellent applicants with an international research portfolio and
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empirical social research in sociology such as causal inference or machine learning or complex panel data analysis. We are seeking excellent applicants with an international research portfolio and network
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for a dedicated PhD student to join our team. Find more information about the Strategic Management area and its members here: http://strategy.univie.ac.at What you will be doing: In
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geological field-based methods and big data applications and machine learning methods. Research focus will be on feedback processes between erosion, sedimentation, tectonics and climate, and topics could
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need expert knowledge in bioinformatic data analysis. Strong expertise in multi-omics data analysis (using R and Python) and a deep understanding of machine-learning models are must-criteria