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both academic research and industrial applications. In addition to theoretical research, the work might involve implementing new algorithms in the SCT tool Supremica, which is developed by the Automation
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algorithms, NLP models, and LLMs to analyze complex data. Designs and implements novel data science methodologies for predictive modeling, causal inference, and probabilistic analysis in clinical and
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modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy systems. Responsibilities will include programming, analysing and interpreting data, and
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-criteria, defining their formalization as fuzzy subsets, and characterizing their uncertainty; Integrating Machine Learning algorithms to better account for low-level sensor data (precipitation, wind-driven
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particle (SEP) events. The project is a joint collaboration of Algorithmics and Computational Intelligence research group at the Department of Computing and Space Research Laboratory at the Department
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on the data recorded in the team, you will develop and test machine learning algorithms for perovskite tandem solar cells' energy yield and degradation Data cleaning and preparation Assisting integration
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, the work might involve implementing new algorithms in the SCT tool Supremica, which is developed by the Automation group. Main responsibilities Conduct research in collaboration with senior researchers and
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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC
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-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
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application. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy