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Description In this project, we develop machine learning models for prediction of optical properties of chiral molecules based on DFT/CCSD data which we calculate ourselves. We include derivative information by
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for transportation prediction, system optimization, and environmental/health impact modeling Deployment of decision-support tools for public-sector clients (municipalities, MPOs, DOTs) Urban mobility, equity
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are analysed using big data and business intelligence applications to monitor tourisms. Additionally, predictive modeling methods are applied to estimate tourist mobility behavior and movement patterns between
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, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12 PhD candidates will be based across seven different universities in Europe: four in
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hydrogenation, dehydrogenation, and hydrogen transfer reactions. Detailed characterization and kinetic studies will be performed to test computational predictions and microkinetic models, and to refine machine
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identification, i.e. learning of models from measured data, and iii) real-time control, e.g. using the model predictive approach. We are working on several projects with industrial partners across the energy
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new vegetation model. The new EEO-based vegetation model should then also be used to predict future transitions and biome shifts to ultimately answer the question to what extent C4 grasslands
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, processing, ad-hoc reporting, and predictive modeling. Develop clear, accurate visualizations to support research interpretation. Maintain up-to-date skills in R and STATA. Presentation & Publication Support
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. The work is part of the regional project “Optimizing Renewable Energy Integration: FPGA-Based Model Predictive Control (MPC) for Grid Stability” (Ref. SI4/PJI/2024-00238) Where to apply Website https
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partners and key stakeholders, including the medical group, hospital, and SOM/HSC; develop rational, data-based and realistic financial models of proposed initiatives to ensure sustainable investment