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Your Job: Chromatography modeling, while crucial for modern bipporcess development, still heavily relies on empirical determination of key model parameters. By combining protein structure
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Midlands Graduate School Doctoral Training Partnership | Loughborough, England | United Kingdom | 3 months ago
of Nottingham to commence in October 2026. Project overview This project investigates how housing inequalities spatially structure wellbeing in cities. It begins from the premise that where we live — and the
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to domain-specific knowledge and research. All three domains – life & medical sciences, earth sciences, and energy systems/materials – are characterized by the generation of huge heterogeneously structured
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overseeing the technical work of students and trainees. Work Performed · Plans and performs experiments utilizing established complex procedures and/or techniques for investigating single-cell genomics
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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collaborative approach to your work, and are comfortable managing multiple tasks and deadlines in a structured, process-driven environment. You communicate clearly and professionally with a wide range of
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will enable predictive simulations of structural, thermodynamic, and kinetic properties in complex systems relevant to catalysis, supramolecular chemistry, and biology. The candidate will benefit from
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advances the Center’s mission to create an affirming, responsive, and data-informed campus environment. Essential Functions: Develop and pilot surveys, focus groups, and structured interviews to evaluate
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explicitly extend these models to capture temporal structure within spike trains thereby moving towards analyses that are sensitive not just to firing rates but also precise timing relationships underpinning
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algorithms Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical