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complexity of genetic evaluations are expanding rapidly. For example, for methane emission different recording techniques might be used, records might be collected at different biological stages or in
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optimisation algorithms for quantum routing using genetic algorithms (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO), optimising cost functions subject to entanglement fidelity
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on high-fidelity modelling and test data for both metals and thermo-set composite materials. To achieve this we will explore the use of advanced genetic algorithms and/or Artificial Intelligence (AI
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of genetically encoded agents, primarily for photoacoustic and fluorescence imaging.Using technologies like photoacoustic imaging, we seek to visualize small populations of labeled cells (e.g. immune cells) deep
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of elements) of the model.; 3) develop an optimization algorithm based on genetic algorithms and metamodels and 4) design functionally graded OC scaffolds using different biomaterials. The doctoral candidate
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); execute PoCs and tech transfer with foundries, equipment/materials/metrology vendors. Data & Platforms: Establish robust data governance and MLOps pipelines; develop reusable algorithms and prototype
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function in similar experiments with different combinations of altered temperature and added parasites. The main work of the PhD candidate entails quantitative genetics experiments testing for G×E. The
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construction and visualization of pangenomes for crops with large genomes Summary: Pangenomes are highly relevant for grains RD&E pre-breeding research because they capture the full spectrum of genetic diversity