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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 21 days ago
that diverse teams and a variety of perspectives enrich our work and our daily collaboration. In a continuous process of learning and reflection, we aim to ensure that all our employees can be themselves and
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consortium. The group offers well-equipped laboratories, a highly collaborative international environment and membership of the Cluster of Excellence "Machine Learning: New Perspectives for Science", which is
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) for simplified data storage and submission Collaboration on tool development for multi-omics data integration (genomics, transcriptomics, proteomics, metabolomics, lipidomics), statistical evaluation and
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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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basic over applied research up to pre-industrial development and is performed in collaboration with national and international partners from university, academy and industry. The institute is part of
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. Responsibilities We offer two PhD positions, eachexploring a different part of this challenge: PhD 1:Teach anaerobes to survive a breath of air. You will push oxygen-sensitive microbes to their limits, uncover how
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for the ERC Advanced Grant project “Equilibrium Learning, Uncertainty, and Dynamics.” **Positions Available** We invite applications for Doctoral Researchers with a strong background in machine learning and an
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well as funding acquisition and (global) outreach, if desired. International networking and collaborations are regarded as an integral part of the PhD research experience and are explicitly encouraged (e.g. South
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processes that produce energy and raw materials. The Department of Thermodynamics of Actinides is looking for a PhD Student (f/m/d) - Machine Learning for Modelling Complex Geochemical Systems. The job
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Systems Biology is an international, enthusiastic, and collaborative team in an outstanding dynamic scientific environment. Research foci in the department are machine learning on graphs, machine learning