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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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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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- conducting processors with respect to practical short-depth (NISQ) quantum algorithms Cooperate and actively work with experimental partners developing quantum processors using these technological platforms
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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | 10 days ago
Experience in HPC computation (application and algorithm/code development) Willingness to closely collaborate with experimentalists and theoretician. Joint research approach of all ERC synergy team members
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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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is to investigate which antigen specificities are enriched in cell subpopulations, depending on the underlying neurological disease. The project will use high-throughput data to develop and apply
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algorithms in extremely complex and enormously large spaces motivated by physics and chemistry Developing interpretable AI for scientific discovery in physics (example here ) Formal mathematics (using Lean’s
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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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and one Postdoctoral position in the field of robot motion and control algorithms for soft material handling, starting September 2025. We are seeking highly qualified and motivated individuals with a
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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