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research is carried out in close collaboration with external partners, both nationally and internationally, and contributes to knowledge development and innovation. Through our collaboration platforms, we
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and deep learning methods for large-scale genomic, clinical, and imaging biobank data, with stable multi-year NIH support. The Zhi Laboratory has a sustained track record of methods development
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the Research Council of Finland (RCF) consortium project focusing on the development of low-field MRI hardware, sequences, image reconstruction and applications, in the Quantitative MRI research group
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DeepMD, NEXMD, i-PI, MixPI, NWChem, CP2K, or comparable packages. Experience with GPU acceleration, distributed training, high-performance computing systems, or C/C++ scientific software development
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project, you will be expected to perform independent research, including data collection from literature sources and online databases, statistical data analyses, development and use of process-based
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crucial role in the selection and sensory perception of food. Achieving a deeper understanding of their chemistry and bioactivity on a molecular level will support the development of sustainable food
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of scientific data, preferably in R Interest and prior experience in the development of external (third-party) grant applications. Ambitions (or previous experience) for developing high level international grant
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technologies, including drones, robotics, and self-driven laboratories. The appointees will engage in research on the development of intelligent hybrid power architectures to support autonomous operation in
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: PhD with wet laboratory experience and an interest in protocol development and experimental design. Experience or training in immunology and flow cytometry is preferred. Experience handling mice and
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writing. · participating in career development activities in the lab. The postdoctoral scholar/fellow will benefit from excellent research experience and a highly supportive and collaborative