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machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. About the Role The post is funded
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to develop generative AI methods for nanoparticle drug delivery design, at the intersection of machine learning, explainability, and pharmaceutical nanotechnology. Job description We are looking for a
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application of machine and deep learning models. The balance between experimental and computational method development will depend on the candidates’ profiles. Start date is expected to be 1 June 2026 or as
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and simulation Prior experience with particle accelerators and/or FELs is highly desirable Familiarity with machine learning techniques is a plus but not necessary Excellent command of English is
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silico approaches. This may include mathematical modeling of biological systems, machine learning and artificial intelligence methods, and the development of innovative algorithms and software pipelines
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model or machine-learning-enabled assets at a company or University). Basic understanding of early-stage technology development. Knowledge of basic principles of intellectual property and licensing
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BSM processes. This will involve taking a lead role in developing dedicated software frameworks, including the implementation of machine learning techniques. A long-term attachment (6-12 months) and
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. Expert knowledge of data modeling, statistical analysis, machine learning, and optimization techniques. Expert in leading and executing complex, high-impact data analytics projects and initiatives
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applications. The project integrates: Computational Fluid Dynamics (CFD) and multiphase flow modeling Radiative heat transfer Machine learning and reduced-order modeling Data-driven optimization for industrial
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research, optimization, and decision analytics. • Supply chain engineering and logistics systems. • Human factors and ergonomics. • Data analytics, artificial intelligence and machine learning