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project around the development of AI models for predicting promising catalyst candidates to integrate molecular modelling techniques, experimental data bases and materials data bases together with novel AI
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. Applicants are invited to propose a research project around the development of AI models for predicting promising catalyst candidates to integrate molecular modelling techniques, experimental data bases and
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computer-based systems and the preparation of data for inclusion in lab books, presentations and publications. Maintain a hardcopy or electronic lab book · Work in compliance with relevant Health and Safety
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, sparsity, and symmetry. Data pipeline: Curate datasets from DFT calculations (and, where relevant, Wannier/TB extractions); implement preprocessing, splits, and rigorous validation. Metrics and validation
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collaboration with project partners for the successful integration of the coatings into real-scale filtration systems. -Documentation and Data Management: Preparation of technical and scientific reports
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, sparsity, and symmetry. Data pipeline: Curate datasets from DFT calculations (and, where relevant, Wannier/TB extractions); implement preprocessing, splits, and rigorous validation. Metrics and validation
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., evolutionary algorithms/strategies, mixed-integer search, multi-objective methods). Strong Python and scientific-computing skills (data handling, experiment tracking, testing, version control). Practical
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of materials and devices following the recently upgraded ISOS protocols and in-situ characterization. Process and analysis of data. Elaboration of periodic reports to keep track of the progress of the project