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advancing analytical methods alongside their implementation in scalable systems supported by high performance computing (HPC) and automated workflows. Example application domains include dynamic population
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multiple Asian cohorts. The position focuses on data harmonisation, statistical genetics, and developing and validating machine learning cancer risk models. Key Responsibilities: Data harmonization
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) classifiers on genomic data in an HPC environment. Develop and employ machine-learning-based tools (e.g., UMAP for dimension reduction) to visualize functional states of cells measured by, e.g., single-cell RNA
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encompassing all or some of the following: HPC, data science infrastructure and tools / software, and diverse domain science application base. Proven ability to successfully work on multiple concurrent projects
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to organize and manage priorities across multiple assigned tasks. Qualify as a United States (US) person as defined by ITAR regulations - A US person is a citizen of the United States, a lawful
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Performance Computing (HPC) resources, research computing systems, and research data storage. The ideal candidate for this position will have excellent technical and communication skills, is curious, creative
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evaluation, or a strong willingness to gain hands-on experience. Eagerness to learn HPC concepts, including parallel computing, distributed systems, and optimization. Analytical skills, problem-solving
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security best practices in HPC environments Strong project management skills, with the ability to manage multiple complex initiatives Excellent communication and collaboration skills, with the ability
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. The chair hosts its own computer cluster and has full access to the high-performance computing infrastructure at ZIH Dresden, one of Germany’s leading HPC centers. Dresden, the capital of the State of Saxony
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Support and High-Performance Computing (HPC) team to ensure network infrastructure supports HPC requirements. Vendor Management: Manage all related vendor support contracts for hardware, software, and