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Field
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using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts generated in the scattering
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This PhD explores light estimation and synthetic relighting of real scenes by combining generative deep learning with computer graphics. It aims to reduce issues such as hallucinations and temporal
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quantitative methodological skills in handling detailed spatial data, including various econometric techniques and machine learning approaches; a thorough understanding of empirical, explanatory research; a
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. You are driven by scientific curiosity, enjoy working with complex multi-physics models, and are eager to advance probabilistic methods, machine learning tools, and simulation techniques. If you thrive
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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scientific coding skills in Python. You are strongly motivated to acquire advanced skills in Python and Fortran and in the use of high-performance computer systems you have affinity and preferably experience
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of scientific computing involving machine learning models for viscoelastic fluid flows. Legislation and regulations: Law Nº. 40/2004, of 18th August, in its current wording (Statutes of Scientific Research Fellow
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point-based PhorEau projections using a machine-learning model predicting tree species richness as a function of spatially explicit abiotic and biotic covariates, including satellite-derived data
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group? Do you enjoy creating complex machines that have never existed before? Do you want to explore physics that nobody else has seen? Maybe you want to join our team as a PhD on our journey
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physical). Solid background in programming and experience with machine learning. Knowledge of participatory design and co-creation methodologies. Ability to learn independently and passion for research