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Field
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prediction of combustion dynamics and emissions in next-generation gas turbine combustion systems operating with gaseous and liquid fuels. • Develop, implement, and validate CFD models for complex reacting
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sequences. You will develop advanced modeling techniques to create privacy preserving realistic data, and predict disease trajectories, outcomes, and other clinically relevant endpoints. Moreover, you will
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focus is on advancing the state-of-the-art in generative modeling and applying these advances toward developing more capable biotechnologies, including the design of more evolutionarily resilient
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Vitro to In Vivo Extrapolation of Toxicant Effects on Ovarian Function” and will focus on phthalates and developing models that extrapolate in vitro assay results to predict in vivo effects on ovarian
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 22 days ago
AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models to uncover how gene expression and mechanical forces interact
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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-derived tumour stem cells and matched iPSC-derived neural stem cells to develop new approaches for personalised prediction of drug responses in glioblastoma, a currently untreatable malignant tumour
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between the University of Plymouth and Cornwall Partnership NHS Foundation Trust, starting in May 2026. About the role The purpose of the role is to develop and apply mathematical models and computational
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integrates real-time data and simulation models to mirror the physical manufacturing and logistics systems. This enables performance monitoring, predictive analysis, and the evaluation of scheduling strategies
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University (ASU) invites applications for two postdoctoral scholar positions in the area of mathematical modeling to work with Dr. Nina Fefferman. The first position will focus on modeling dynamics