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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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causing diabetes mellitus, and to develop therapeutic avenues. The group is particularly interested in understanding how genomic information is used to direct cell-specific gene expression, and to pinpoint
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remain healthy for many years or why their health eventually declines with advanced age. World-wide data show that more than 25% of female fertility problems are unexplained, pointing to a huge gap in our
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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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(S)TEM. Requirements: Education: MSc in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science. Knowledge: Deep expertise in electron microscopy, particularly STEM and FIB methods
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topics in astrophysics and cosmology using LIGO-Virgo data. This includes searches for compact binary coalescence events and determining their mass/spin spectrum, and for primordial black holes and axion
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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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topics in astrophysics and cosmology using LIGO-Virgo data. This includes searches for compact binary coalescence events and determining their mass/spin spectrum, and for primordial black holes and axion
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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