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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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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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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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Information Science, and for about 30% of the Bachelor’s and Master’s programmes in AI at Radboud University. iCIS employees have a high level of freedom to determine the way they structure their work. Over the past
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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
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Directed Energy Deposition (DED) process for metallic components. The PhD candidate will focus on edge computing and the application of AI for data analysis and for identifying correlations with ground truth
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better The right place for IMPACT. SnT researchers engage in demand-driven projects. Through our Partnership Programme, we work on projects with more than 45 industry partners Multiple funding sources