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. Extracting this information is still a challenge though, given its intrinsic multidimensional nature (time, perturbation, tissue position, cell state, statistical power). With modern probabilistic modelling
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implementation (Month 22–24), · Hitachi Energy: Benchmarking of magnetic components (Month 30–32), · Danfoss: Experimental model verification (Month 34–35). IMPORTANT: Strong knowledge in
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mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative
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on the design, optimization, and implementation of integrated quantum sensor architectures. As part of a multidisciplinary team, you will explore innovative, energy-efficient approaches to precision sensing in a
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Genetics, Reproduction and Development (GRAD) Large Research Group to: Use single-cell omics techniques to analyze human pluripotent stem cells, human embryos, and stem cell-derived embryo models. Study how
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FLOW research group is a young, dynamic group working in the fields of thermodynamics, fluid mechanics, and data-driven modelling. At the Department of engineering Technology (INDI) — Thermo and Fluid
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will contribute to the enhancement and implementation of a digital model representing the Luxembourgish and cross-border energy landscape. The doctoral student will be a member of the Doctoral School
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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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(e.g. CO, methanol, ethanol) using solar energy is one of the most appealing strategies to address global climate change and shortage of energy supply but it requires highly efficient photocatalysts
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consumption while guaranteeing optimal power production. You will work on the cutting edge of both wind energy and machine learning, two of the fastest growing scientific disciplines, to develop graph-based