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HiPerBreedSim project. In this role, you will leverage recent advances in working with ancestral recombination graphs (ARGs) to develop algorithms and code for simulating population genomic data, including
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: Engage with clinicians and patients in a collaborative co-design process to develop and test (using health psychology theory and methods) innovative, evidence- and theory-based tools, including: (i
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agents, including uncertainty quantification at the agent’s level. The project will bring together ideas from Statistics, Probability, Statistical Machine Learning, Statistics and Game Theory and is an
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Dependent type theory is a foundation of mathematics that allows us to mechanise arguments, and is closely related to higher category theory. It serves as that basis of programming languages and
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John Williamson, and Dr Sebastian Stein. The job requires the proven ability to develop novel theory and build and evaluate working interactive prototypes involving complex computational models
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adaptive trials, stepped wedge trials and cluster trials. You will have good knowledge of applied statistics and statistical theory, especially for pragmatic trials and complex randomisation systems. You
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publications on AI security. You will also prepare working theories and analyse data from a variety of sources, contribute ideas for new research projects, act as a source of information and advice, and
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) in one or more of the following areas: Quantum machine learning, Quantum algorithms, Quantum information theory or Theoretical Physics Essential criteria: Proficiency in at least one programming
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Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial Optimisation Commitment to Diversity The University
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Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial Optimisation. Commitment to Diversity The University