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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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terrestrial networks, non-terrestrial network entanglement distribution. Your profile PhD degree in wireless communications, signal processing, machine/deep learning or a closely related field in Electrical and
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. Experience in high-throughput sequencing data analysis and cluster/cloud computing. Proficiency in variant calling, single-cell DNA and/or RNA analysis, and machine/deep learning (preferred but not required
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/performance trade-offs and typical RAN levers; experience with energy metering data is a plus. • Strong background in AI / Machine Learning for decision-making (e.g., forecasting, optimization with learning
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mechanistic chemical biology research, the postdoctoral fellow will learn and gain experience in small molecule drug discovery from target validation, chemical probe discovery to preclinical mechanistic studies
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designed to prepare postdoctoral researchers for a successful ERC Starting Grant application or equivalent and for an independent research career in top research organisations in Europe and around the
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mechanistic chemical biology research, the postdoctoral fellow will learn and gain experience in small molecule drug discovery from target validation, chemical probe discovery to preclinical mechanistic studies