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physics-integrated machine learning models—to predict, analyze, engineer, and understand microbial community dynamics. Applications span precision medicine and built environment microbiomes, with a strong
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Computational modelling of two-dimensional graphene-based materials School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Natalia Martsinovich Application Deadline
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inviting dynamic young scientists, capable of theoretical fracture mechanics and related modeling techniques, to join our team to probe cutting edge issues in fatigue and fracture. Some examples of research
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Details The aim of this project is to combine nanomechanical methods with modelling (i) to develop quantitative, predictive models for the behaviour of molecules in sliding contacts, and (ii) to understand
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colleagues on multi‑omics data integration and analysis. You will also work with AI experts to help implement predictive models that improve guide design and functional genomics workflows. You will join an
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colleagues on multi‑omics data integration and analysis. You will also work with AI experts to help implement predictive models that improve guide design and functional genomics workflows. You will join an
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software for aerospace precision machining — you will develop physics-informed machine learning models that learn how individual machines actually behave, and use those models to drive a genuinely
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learning workflows, and developing complete models. Example applications include drug design, cryo-electron microscopy, structural prediction and dynamic simulation of biological macromolecules, genomics
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
: https://www.list.lu/ How will you contribute? You will be part of LIST’s Remote sensing and natural resources modelling group Embedded in the Environmental Sensing and Modelling (ENVISION) unit
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rigorous quantitative description of phenomena predicted by theories such as K41 and Onsager, which still lack a full mathematical justification. The researcher will work on linear advection–diffusion models