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Bioinformatics, Computational Biology, Computer Science, Biomedical Engineering, Computer Engineering, Genetics/Genomics or related field experience with ‘omics platform output experience with biological datasets
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Applications are invited for a Research Assistant/Associate position to work in the groups of Dr Felipe Karam Teixeira and Professor Richard Durbin at the Department of Genetics in central Cambridge
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involves the use of quantum chemistry, machine learning, and genetic algorithms to search for new homogeneous chemical catalysts. Who are we looking for? We are looking for candidates within the field
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Project advert We are looking for a PhD candidate with a background in human physiology or biomedical engineering/computer science to develop new ways to measure healthy ageing from genetic and
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for combined network design and resource pricing, incorporating fairness for different transport aspects (e.g., accessibility, emissions, safety) employ state-of-the-art metaheuristic algorithms (e.g., genetic
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of peptide design and chemistry, computational methods (machine learning, deep learning, genetic algorithms), microbiology, synthetic biology, and related areas essential to developing novel
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theoretical research is focused on embodied neuroAI, recognising that the body influences biological neural networks, the continuity of actions, and sensory inputs. Leveraging advancements in Drosophila genetic
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analysis, with possible specialisations in genomic and molecular biology techniques as well as in algorithms, statistics and artificial intelligence for molecular genetics. This is based on perspective and
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maintenance. 4. Automated Design Optimization: Reinforcement learning and genetic algorithms will be applied to optimize CFDST geometries and material configurations for maximum efficiency and durability. By
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, incorporating fairness for different transport aspects (e.g., accessibility, emissions, safety) employ state-of-the-art metaheuristic algorithms (e.g., genetic algorithms, simulated annealing) to solve large