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The Data Integration in the Life Sciences (DILiS) research group at the Institute of Computer Science at Freie Universität Berlin is headed by Katharina Baum. We primarily investigate and develop
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. Requirements: • Bachelor's degree in computer science or equivalent knowledge Desirable: • Knowledge and practical experience in areas such as algorithms, data structures, software engineering, artificial
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Physics, Chemistry, Applied Mathematics, or related fields. Desirable: Fluent English, spoken and written Strong background in computational Physics and Statistical Physics Previous work experience in
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Responsibilities: The successful applicant will cover research and teaching in the field of data-driven material research with a reduced teaching load of two weekly credit hours at Freie Universität Berlin. The professorship includes the opportunity of establishing and leading a newly founded...
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The Planetary Sciences and Remote Sensing research group at the Institute of Geological Sciences at the FU Berlin participates in various experiments in space missions of ESA, NASA and JAXA. In our
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: economic sociology, historical sociology, (historical) political economy, historical social science, computational sociology - advanced skill in quantitative, statistical methods (R, stata, Python), ideally
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approaches to electrolyte systems Requirements: Candidates must have a Ph.D. in Physics, Chemistry, Applied Mathematics, or related fields. Desirable: - English language fluent, spoken and written - excellent
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Berlin, and the Max Planck Institute in Hamburg. Our work combines physics, chemistry, and materials science - pushing the frontiers of quantum materials research. The overarching goal of the Collaborative
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medicine in accordance with the German Veterinarian Licensing Regulations (Verordnung zur Approbation von Tierärztinnen und Tierärzten), and cover teaching in the equine science bachelor’s degree program
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of accounting, corporate reporting in a national and international context, and auditing issues. Methodologically, the focus is on quantitative empirical approaches, in particular computer-aided text analysis