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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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robust descriptors (e.g., water activity, sorption, glass transition temperature, plasticization, porosity, internal distribution) and provide predictive guidelines to rationally select and design drying
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, the work seeks to establish predictive fingerprints of metal-ion mobility and uncover general principles linking structure, bonding, and dynamics. Particular emphasis will be placed on understanding both
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on the project can be found here: https://hecustom.eu/ This post will contribute to the creation and validation of a digital twin (with biological bone models) to assess and interrogate the issue of
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approaches capable of guiding experiments, interpreting results in real time, generating predictive models of materials synthesis processes, and refining experimental strategies under a Human-In-The-Loop
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intelligence models for the analysis of multispectral remote sensing imagery. The main tasks include implementing computer vision and machine learning methods for the detection and prediction of algal blooms in
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speech processing, a research question that is rapidly gaining in importance. The project is centred on the hypothesis that the cerebellum conveys predictions about upcoming speech sounds to the neocortex
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. Combining AI-based prediction (e.g., TCNN, LSTM, etc) with musculoskeletal models to estimate and predict muscle activation and tendon force over short horizons (e.g. ~200 ms). Integrating these predictions
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model predictions with biological knowledge and external data sources. Work closely with academic partner groups and the Innovation & Business (I&B) team to align technical development with biological
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: you will divide your time in equal shares between teaching GISc at bachelor and master levels, and advancing research about the integration of statistical movement models with predictive simulation