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. Potential approaches include data-driven parameter estimation for white-box models, the development of black-box models using machine learning methods (e.g., neural networks) and their combination
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and apply AI and machine-learning methods to infer interaction networks from acoustic co-occurrence and activity patterns, and analyse their structural properties to link topology to response diversity
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machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch or TensorFlow) Project funding Other Funding
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-based metal additive manufacturing . The role will focus on thermal field modelling, multi-physics numerical simulations, machine learning and process parameter optimization. We expect the candidate
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research program in computational oncology, centred on new quantitative and machine-learning methods, including artificial intelligence and generative modelling, that integrate single-cell genomics and
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. Bioinformatics analysis of data will include use of population genomics techniques, phylogenetics and machine-learning development. The successful candidate will be expected to write up results as manuscripts
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projects here: https://dorienherremans.com/biblio (including work on models like Mustango, SonicMaster and other generative systems). The ideal candidate brings a strong machine learning foundation
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will combine first-principles atomistic simulations, scientific machine learning, reaction modeling, and integration of computational and experimental data. The associate will develop reproducible
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ultimately shifts in species' distributions. This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species
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Primary supervisor Adamu Muhammad Buhari Research area Machine Learning Vision-Language-Action (VLA) models are changing the way robots perceive their surroundings, interpret instructions, reason