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design-oriented or empirical methods is considered an advantage. Initial publication successes in renowned scientific journals are expected as well as initial experience with both publicly and industry
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. YOUR RESPONSIBILITIES Develop an original research program that leverages unique numerical experiments to test different hypotheses or uncover novel mechanisms Supervise doctoral and postdoctoral
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The Faculty of Mathematics and Computer Science at the University of Göttingen invites applications for a temporary professorship with civil servant status (grade W1 NBesO) with tenure track (grade
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Associate Professor of Experimental Physics Focusing on AI-Based Research of Biomolecular Structures
elucidation using crystallographic methods. The focus of the research should be on the development of AI-based analysis methods for macromolecular single crystal diffraction data. Furthermore, experience in
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ecosystem and provides an excellent environment for professional growth and development. What you can expect Independently develop and apply computational methods to map molecular trajectories from normal
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integration as well as multimodal data analysis should ideally incorporate user-centered methods for the participatory development of new digital health technologies in order to develop and apply patient
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-party funding (e.g., EU, DFG; BMBF). The professor is expected to employ a wide range of methods encompassing modern optical, molecular biological, biochemical, and/or cell biological methods aimed
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are seeking to appoint an internationally highly visible researcher engaged in the field of Physical Chemistry of Molecular Systems with a focus on Experimental Method Development. The scientific activities
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Research Group Leader (f) focusing on research into polymeric and self-organized materials, to be investigated with modern experimental methods with regards to their structure and dynamics. As a junior
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of interest include, but are not limited to: AI methods that meet the complexity of living systems, high-dimensional machine learning for biology, statistical machine learning, AI‑driven laboratory automation