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engineering, energy informatics, or a related field Solid programming skills, ideally in Python, and experience or interest in data analysis, machine learning, modelling, or simulation of energy systems
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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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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
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University Computer Center, Research Services and Career Development), as well as Data Stewards from other faculties/centres, you will further develop the RDM at FGGA and University. You can expect a diverse
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approaches in Human-Computer Interaction and Contextual User Experience. The Center for Technology Experience is acting in a variety of challenging application contexts. We are passionate about addressing the
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through advanced machine learning and visualization techniques. As part of our vibrant and interdisciplinary team, together with the also newly created positions of Ph.D. Machine Learning and of Ph.D
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studies. The successful applicant will get a contract as university assistant (prae doc) and will be working in the research group “Data Mining and Machine Learning” at the Faculty of Computer Science. Your
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of these methods to problems in the physics of oxides, semiconductors and their surfaces. Machine learning methods will be used to close the complexity gap. Applicants will have outstanding achievements or show
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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