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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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(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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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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training more data-efficient and to speed up candidate selection. Main Tasks and responsibilities: Core duty 1: The person shall deploy and interconnect an active learning framework into our simulation
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interfacing, thin-film technology and nanofabrication, growth and characterization techniques and neural data analysis. The research assistant will specialize in multiple facets of neural interfaces development
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replication: use fully converged simulations as references; design experiments to measure convergence speed, accuracy, robustness, and cost vs. baseline workflows. Agent & data pipeline engineering: build
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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