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! The Clinic focuses on inflammatory diseases of the central nervous system, especially multiple sclerosis. As part of the Department of Neurology, we study the pathogenesis and treatment of this and other
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 1 month ago
project within the SusMax network focused on developing interpretable machine-learning frameworks for kinetic multiphase reaction-network discovery in the catalytic conversion of renewable feedstocks
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sensor data, with applications in disease modeling and the development of material science-based innovations. These efforts aim to optimize system performance and uncover novel biological insights in close
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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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Max Planck Institute of Biophysics, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | about 1 month ago
(imaging) data analysis is preferred. Prior experience in microscopy is desired but not required.) Development of 3D diffusion-based single-molecule sensors (the candidate with theoretical knowledge and
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the Metabolomics Core Technology Platform (MCTP). We seek an enthusiastic scientist who will operate, develop, and continuously advance spatial metabolomics services, facilitating cutting-edge research across
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. The project’s overarching goal is the development of digital quantum algorithms for the simulation of non-abelian lattice gauge theories. We are looking for highly motivated individuals, with the desire
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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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algorithms into an existing framework, with a focus on efficiency, as well as creation and execution of relevant simulation pipelines: from real data to mathematical and clinically actionable results