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number variations, are major risk factors for adverse mental health outcomes. Beyond their clinical importance, they provide powerful human models for uncovering multiscale genetic mechanisms in psychiatry
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framework capable of accurately predicting pollutant transport and dispersion in coastal waters. By combining high-fidelity numerical simulations with data driven surrogate models, the proposed research aims
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extensively with chemists, materials scientists and engineers. His laboratory uses a broad range of disease models including Drosophila, rodents, human neurons and brain organoids generated from patient-derived
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experiments, behavioral research, econometric and causal inference approaches, optimization and analytical modeling, and data-driven techniques such as machine learning and large language models. Our work is
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across the Centre, in accordance with the Theory of Change and evaluation protocols Undertaking evidence reviews Promote equality and value diversity, acting as a role model and fostering an inclusive