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using existing NPL datasets. The work will integrate suitable physics-based models (for example PV performance modelling, electro-thermal and thermofluid dynamics) with deep learning and multi-fidelity
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manufacturer and technology company. Applications are invited for a 3.5-year EPSRC funded UDLA PhD studentship. The studentship will start on 1st October 2026. Project Description As Deep Learning and
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and spoken English. Desirable: Experience with photonic/electromagnetics design software. Familiarity with deep learning platforms (e.g. TensorFlow, PyTorch). Funding and eligibility The project is
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latent coordinates; ii) Cross-modal alignment (e.g., canonical correlation analysis and Deep CCA) to align heterogeneous parameterizations and modalities in a shared latent space, iii) Operator learning
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, machine learning techniques, etc.) is desirable. This thesis offer within the AstroParticle and Cosmology Laboratory (APC) is part of the Deep Underground Neutrino Experiment (DUNE). DUNE is an
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- Knowledge in programming in Python or R - Familiarity with machine learning or deep learning methods is a plus - Interest in plant genomics, evolutionary biology, or comparative genomics - Proficient in
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interdisciplinary team with clinicians and engineers; You have strong programming skills in Python; You have knowledge of medical image processing, and machine learning and deep learning techniques; Written and
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analysis utilizing methods rooted in artificial intelligence (i.e. machine learning and deep learning). The analysis will be the basis for developing a predictive model to help select the most optimal method
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environment. Key research Objectives: AI Innovation (Taxonomic Identification): Developing and optimizing deep-learning architectures (e.g., YOLO) for the automated detection and classification of nocturnal
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deep learning frameworks Have experience or strong interest in mechanistic interpretability, representational geometry, or computational neuroscience Be comfortable working across disciplinary boundaries