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-0831 Description of Work: At the Digital Twin Innovation Hub, we are developing infrastructure for the construction, simulation, analysis, and visualization of a human immune system model that represents
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nursing science, applied to nursing practice and care processes, including digital care pathways, predictive and preventive models, and data-informed clinical decision-making. The professorship emphasises
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patients with early DKD and matched healthy controls. ▪ Development of an AI-driven digital African twin model capturing population-specific molecular heterogeneity, disease trajectories, and predictive
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neuroscience • Psychological and brain sciences • Medical • Biomedical • Nanofluidics and microfluidics • Biomimetics and biofilms • Social media analysis and predictive modeling • Molecular, cellular
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activities under the supervision of João Lacerda: Optimization of T cell therapies Phenotypic characterization of T cell population by flow cytometry Development of in vitro cancer models to study CAR T cells
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capacity. We are increasingly incorporating machine learning models into our design-build-test iterations to predict protein function. The group’s focus is predominantly enzyme design, but readily touches
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industrial partners and spin-offs, together with KTT Business Developers and Innovation Managers. Develop standardized, technology-adapted licensing models and agreement templates (exclusive/non-exclusive
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of developing a digital twin model of renewable energy generators”, 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), DOI: 10.1109/ISIE.2019.8781529 [4] https://uk.mathworks.com
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systems whose near-field radiative capabilities allow for focusing and localizing EM energy. Predictive models, based on EM simulations and extensive measurement campaigns, will also be developed
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and edge computing; The design of architectures/models which accurately capture the complexities of the data, with robust estimates of confidence in predictions and compressed quantities of interest