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, to define novel biomarkers, and to identify novel therapeutical targets. We have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models
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challenges of learning from network traffic, (ii) train original AI models that are designed to operate precisely on such data, and (iii) demonstrate the viability in production of AI-driven solutions for, e.g
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optimization of laser deposition coating processes using combined wire and powder feed, including numerical simulation of laser modelling, and process parameter optimization. Legislation and regulations: Law Nº
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materials science by integrating physics-based simulations with data-driven analysis of cutting-edge synchrotron radiation facility data. By combining experimental data with physical models, we establish
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datasets, modelling approaches, and performance metrics; develop physics-informed and data-efficient machine learning models to predict sorbent behaviour from sparse and multi-modal experimental data; and
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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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at the intersection of mathematics, computation, and cancer biology. We develop mechanistic, predictive models of cellular decision-making to address fundamental and translational challenges in cancer, including drug
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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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Details A brain decoding model aims at predicting what sensory stimulus is received (e.g. visual stimuli, different images), which mental state is experienced (i.e. asleep, awake, drowsy) or even what is
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. The candidate will reduce model uncertainties by producing new large cosmological simulations of the magnetic outputs from galaxies in the ENZO code, which will test realistic implementations of baryonic feedback