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developing solid teaching skills. You are willing to work with problem-based learning and to involve students actively in their own learning processes through project work, supervision and dialogue-based
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complex materials simulations. These agents will assist with setting up, executing, and optimizing electronic structure workflows, from standard ground-state Density Functional Theory (DFT) calculations
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/ TensorFlow / Scikit Learn Highly knowledgeable in mathematical and statistical concepts Solid foundation in mathematics for Machine Learning (Linear Algebra, Probability, Optimization). Proficient in English
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engineering, or a closely related field by the time of enrolment. The successful candidate should demonstrate the following qualifications and competences: A solid academic foundation in data analysis, machine
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-explicit ground-state models of supported and unsupported nanoparticles. Modeling catalytic reaction kinetics to identify active sites and reaction barriers, integrating experimental TEM data. Comparing
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good process simulation and sustainability assessment skills. The FrameBio PhD project, is part of prestigious Marie Skłodowska-Curie Actions (MSCA) Doctoral Network, a collaboration between 16 partners
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engineering, or similar. Solid mathematical and analytical skills, including signal processing and optimization. Knowledge about classical and/or quantum data communication, including for instance error
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exploring the chemical and physical principles of peptide ionization and fragmentation in LC-MS/MS – with particular emphasis on the analytical potential of negative ion mode. Current proteomics workflows