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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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fluids, flow-induced pattern formation in both simple and complex flows (e.g. flow instabilities, product defects), multiscale analysis, and the application of machine learning techniques. About the
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score derivation and validation, and other relevant analyses. Develops R or Python scripts for data analysis, statistical modeling, and machine learning techniques, ensuring reproducibility and efficiency
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to mentor students, teach/train other researchers in LCA tools, and develop independent research projects as desired. The successful applicant will possess a PhD in chemical engineering, chemistry
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, or other Maxwell solvers. Experience with machine learning algorithms is an advantage but not required. General qualifications Scientific production and research potential at the international level
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simulations using, e.g., COMSOL, Lumerical, or other Maxwell solvers. Experience with machine learning algorithms is an advantage but not required. General qualifications Scientific production and research
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us to explore the relation between the degree and type of processing, and the foods that result from their use. The results will be used for data machine learning, in collaboration with other partners
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high