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broad experience in the development of electronic structure methods and their application in order to perform atomistic simulations of molecules and materials. These include (but are not restricted
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of digital data in DNA. -Contribute to designing and optimizing methods for DNA digital data storage in nanomaterials. -Collaborate with interdisciplinary teams, including molecular biologists, chemists
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environments, specifically Computer Vision, Machine learning algorithms and methods for rock characterization, fragmentation prediction, and mining optimization. Specific Requirements Good academic and
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, particularly STEM and FIB methods. Proven experience in designing and conducting in-situ TEM experiments. Familiarity with energy-related nanomaterials and catalysis is highly desirable. • Professional
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: Integrate uncertainty estimation (ensembles or evidential methods) to prioritise new calculations and improve data efficiency. Integration: Export learned Hamiltonians to downstream simulation tools for band
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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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biological datasets (e.g., next-generation sequencing, proteomics, imaging data). A solid understanding of statistics and data analysis methods. Ability to work independently and take initiative and solve
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characterization techniques. · Knowledge: Deep expertise in electron microscopy, particularly STEM and FIB methods. Proven experience in designing and conducting in-situ TEM experiments. Familiarity with energy
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broad experience in the development of electronic structure methods and their application in order to perform atomistic simulations of molecules and materials. These include (but are not restricted
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and 2D materials, and oxides. Fabrication of functionalized Pb-free MHPs by solution processing methods. Fabrication of complete Pb-free MHPs solar cells and memristors (TFTs). Stability analysis