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) with computational methods. The candidate will obtain single-molecule multiplexing data and validate machine learning predictions using the high-throughput data. The successful candidate will collaborate
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& evolution Proteostasis & phase separation Within these projects, you will have the chance to work closely with top scientists, learn valuable skills and get training in the latest techniques. See our research
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Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing power, latency, and memory bandwidth issues with reliable
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pharmacology, to better understand the complex interplay of the many factors that drive cardiometabolic disease. You can learn more in the Executive Summary of CBMR's Strategy 2024–2028 . CBMR was established in
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-trivial software. Solid understanding of algorithms, numerical methods, scientific computing, or machine learning. Ability and motivation to write clean, maintainable and well-tested code. Strong interest
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information systems or are willing to acquire such skills through dedicated training during the first year. Terms and Conditions An employment contract for 4 years, with an initial one-year contract subject to
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plasticity in the organoid reshapes functional connectivity during learning. The aim is both to advance OI as a computing paradigm and to use it as a window onto the cellular and network correlates of learning
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machine learning and AI, probabilistic risk modelling, hydrology and actuarial science. We realize that candidates will usually have expertise in one of these fields and ask for a genuine interest in the
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 2 months ago
and advance statistical, image analysis, and machine learning approaches Develop interpretable models that reveal biologically testable relationships and generate new scientific hypotheses Independently
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. Using an innovative multilevel approach, you will move beyond institutional averages to identify where inclusion succeeds, where disparities persist and how universities can create more equitable learning