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. Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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reduction and uncertainty quantification for biological flows. The goal of the project is, in particular, the development of robust methods to quantify the propagation of domain uncertainties in
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Max Planck Institute for Mathematics in the Sciences | Leipzig, Sachsen | Germany | about 8 hours ago
. Close interaction with other research groups at the institute, in particular with the nonlinear algebra group of prof. Bernd Sturmfels, will be possible and encouraged. We are currently developing some
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PostDoc in "Sustaining the keystone: Rethinking Antarctic krill fishery management under climate ...
waters are experiencing an increasing intensity of commercial, scientific, and political developments. The Southern Ocean is no longer a remote region (if it ever was). Scientists, fishers, and tourists
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purpose of this position is to conduct research in the field of model reduction and uncertainty quantification for biological flows. The goal of the project is, in particular, the development of robust
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timings) affect the metabolome and proteome of rapeseed seeds. Your findings will serve as molecular fingerprints to support Deep Learning models for hybrid development. Whom we are looking for: An early
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development and perform moderate teaching duties. Applicants are required to hold a PhD in physics or an equivalent doctoral degree. Applicants should provide a curriculum vitae, a list of publications, a brief
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available in the Division of Proteomics of Stem Cells and cancer, headed by Prof. Jeroen Krijgsveld. Our division develops innovative proteomic approaches to understand proteome regulation in cancer, both in
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development. It is one of the world's leading research institutions in its field and offers natural and social scientists from around the world an inspiring environment for excellent interdisciplinary research