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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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for supply chain and marketing optimization. The project will integrate machine learning, deep learning, foundation models, and interpretable AI approaches, ensuring scalability, robustness, and industrial
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are essential Additional qualifications Experience and courses in one or more subjects are valued: statistical machine learning, optimization, deep learning and signal processing. Rules governing PhD students
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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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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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bring: A PhD or equivalent qualification, with a distinguished record of leadership in teaching and learning at the tertiary level. An internationally recognised and sustained track record of impactful
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on machine and deep learning methods for analyzing the heterogeneity of microbiota and inferring activities of biological pathways. The Institute provides an international and interdisciplinary research
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teaching/learning process and for individual students. Your experience includes: Terminal academic doctorate required (PhD, EdD, DSc). Must meet and maintain the qualifications and standards set forth by any
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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, training deep learning models on large satellite datasets, and contributing to the integration of forecasting workflows into the IceBox system. The candidate will work closely with an interdisciplinary team