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Field
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) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs
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machine learning frameworks like PyTorch. The candidate should demonstrate strong analytical abilities, an eagerness to learn and solve complex problems, and possess effective collaboration skills
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(HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution
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critical steps of gastrulation and early development. Creating iPSC lines with mutations in elements of the GAG biosynthetic machinery. Applying novel GAG analytical technologies to investigate how changes
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specifically on tumour metabolic targets. You will develop and support the INSTINCT-MB. programme through the provision of experimental, analytical and bioinformatic expertise. This will include the development
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, Machine Learning, AI, and related areas including Optimization, Mathematics and Physics are welcome to apply. The candidate is expected to conduct independent research and should have strong analytical
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validation, and regulatory reporter assays. Learn more about the group here . YOUR MISSION: Investigate how genetic variants affect male reproductive function Perform single-nucleus and bulk RNA sequencing
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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analysis and analytical data analysis workflows, together with other team members Implementing AI-based microscopy image analysis software as python packages Developing algorithms to deploy machine learning
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biosynthetic machinery Applying novel GAG analytical technologies to investigate how changes in GAG structure and organisation drive and/or respond to shifting developmental stages. Working Environment: Based