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the Department of Energy Sciences. At the division, we conduct research in various fields, including research in design and optimization of turbomachinery, reactive fluid dynamics, multi-phase and turbulent flows
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dynamics, such as hidden Markov models or statistical jump models, affect the optimal decision-making process for an investor. Specifically, we aim to develop new methods for regime models, including
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consists of 18 research groups covering a wide range of mathematical disciplines – from pure and applied mathematics to numerical analysis and optimization, as well as mathematical statistics and machine
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maximal signal-to-noise and detection of picomolar levels of protein in the blood. The laboratory’s part of the project is to test and optimize this new strategy and subsequently use it to evaluate
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while optimizing sequence properties. An emerging frontier focuses on designing proteins that act as templates for inorganic interfaces, forming symmetric oligomers that control inorganic material
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of proteins with near-atomic accuracy. Models such as RFdiffusion, LigandMPNN, and hallucination-based frameworks can now generate symmetric oligomers, cages, and backbones while optimizing sequence properties
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and the Faculty of Medicine in order to optimize the conditions for preclinical and clinical translational research, research strategies and development, as well as education on both undergraduate and
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the use of crystallographic software and data processing pipelines Experience working with computation clusters and managing large datasets Proven ability to develop, maintain, and optimize scientific