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motivated Neuroscience postdoctoral fellow. In addition to neuroscience research experience, having familiar with machine learning/AI/ big data processing will be an asset. A major part of this PDF
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postdoctoral researcher, you will use various techniques to investigate fiber-reinforced structures in nature (i.e., fungi), including additive manufacturing, computer simulations, materials testing, and
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and platinum complexes with long carbon chains. Collect spectroscopic data (NMR, IR, UV/Visible, etc). Collect and interpret crystallographic data, and undertake physical measurements. Participate in
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-of-the-art sparse algorithm in matrices, tensor and networks for large-scale numerical, scientific and AI models and disseminating findings through publications and presentations in top-tier peer-reviewed
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
Understanding the individual and relative motion of particles is fundamental to understanding transport processes in complex flows. Large-scale transport in atmospheric flows is well understood and measured by
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for more information. About you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD degree in Engineering, Computer
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Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities
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, diet, and immune system to investigate their roles in various diseases, including but not limited to cancer, metabolic disorders, and infections. The research design involves the integration of large
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opportunity to be part of large-scale experiments tackling pressing societal challenges. You'll be involved in every stage of the research process—from experimental design to data analysis and publication—while
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interest and documented skills and experience in using computer-based tools to analyse, simulate and predict capture performance of active and passive fishing gears. A track record of publishing in peer