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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | about 1 month ago
holography. We are seeking a highly motivated postdoctor-al researcher to join our multidisciplinary team at the intersection of optics, electronics, machine learning, and atmospheric science. The successful
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protein biochemistry, single particle cryo-EM or cryo-ET is an asset, curiosity and willingness to learn new methods and adjust to technological developments a must. Strong written and oral
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projects and deadlines Scientific track record Fluency in English; German proficiency or the willingness to learn is advantageous Familiarity with data-analysis / scripting tools (e.g. SCiLS Lab, METASPACE
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 13 days ago
learning. It also offers the opportunity to work with data from the European XFEL facility at DESY. Project website Your profile Eligible candidates have strong skills in computational physics and
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, initiative/commitment, ability to work in a team and willingness to cooperate, willingness to learn We offer: Interdisciplinary research at the interface of politics, economics and society Work in national and
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reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming languages (C++, Python, or Julia) is highly relevant. Knowledge
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Tübingen offers a combination of high-performance medicine and strong research. The goal of the Carl-Zeiss-Project “Certification and Foundations of Safe Machine Learning Systems in Healthcare” is to enable
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
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areas is expected: numerical analysis, scientific computing, model reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming
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of machine learning and health sciences, with unique access to experimental and clinical data. Embedded in Munich’s thriving AI landscape, fellows benefit from world-class facilities, interdisciplinary