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background are discouraged from applying unless their profile also includes a substantial empirical element. The successful candidate will be close to completion or have a Ph.D. degree from a recognized
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chemistry spans organic molecules, transition metal complexes and carbon nanomaterials. Job description The foci of the project are to: Design and execute new synthetic routes to organic molecules, transition
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and policy choices Effects of intergovernmental fiscal transfers Fiscal decentralization and residential sorting The work might be based on theoretical modelling and/or empirical analysis. Profile
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analysis, and/or qualitative/quantitative methods. A demonstrated interest in topics pursued by the Chair team, such as knowledge creation, knowledge management, innovation processes, digital technology and
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biological data analysis as well as microbiology and biotechnology techniques. You’re always up for a challenge, eager to learn new methods and technologies, and thrive in a fast-paced research environment
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cell tracking and quantification, large volume 3D bone marrrow imaging with single molecule sensitivity, and ai-supported computational analysis. Job description We are seeking a highly motivated postdoc
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component, but an engagement with critical AI studies, design approaches, game studies, code studies, and/or ethnographic frameworks would be welcomed. Also desirable would be experience (or interest) in
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Apply machine learning techniques for data analysis and time-series forecasting Collaborate in a multidisciplinary team to accelerate functional thin film development Your work will be part of a larger
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residual stress analysis. The objective will be to investigate these phenomena using simulation tools and via sophisticated experiments. Besides, the candidate is expected to supervise BSc-/MSc students
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are required to have: A completed PhD degree. Experience in machine-learning methods. Some skill in at least one of these topics: Large data sets analysis Statistics and uncertainty analysis (probabilistic