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programming skills in R and/or Python; experience with shell scripting. Hands-on experience with sc/snRNA-seq analysis tools (e.g., Seurat, Scanpy, Cell Ranger). Familiarity with high-performance computing
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profound knowledge in computational and theoretical physics/chemistry. Capability of team work is essential. Skills in high-performance computing, materials chemistry, theoretical chemistry, molecular
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and waste heat sources will need to integrate multiple supply options with varying temperature levels. To support effective planning, energy professionals at the district and city level must be able
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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diagnostic capabilities. The skills and knowledge gained will be transferable to other applications requiring high-performance radiation detection and advanced material interfaces. Through
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Position Description The Unsteady Flow Diagnostics Laboratory (UNFoLD) led by Prof. Karen Mulleners at EPFL in Lausanne is looking for multiple PhD students to join the group in the fall of 2025 or early
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This PhD project will develop next-generation grid-scale energy storage solutions integrated into HVDC (High Voltage Direct Current) systems at the University of Edinburgh, in partnership with UK
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data through advanced ex-situ measurement technologies. The group’s ambition is to develop a robust framework for capturing high-quality data from their in-house hybrid additive-subtractive research
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perform measurements of AI algorithms to fill in the unknowns uncovered in such a data flow diagram. The energy scalability of the core algorithms of a new nationwide AI system can be predicted using
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Master's theses participation in national and international research projects The position comes with access to high performance computing resources and access to training opportunities within ScaDS.AI