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that help determine when an AI model is ready for use and when more research is needed. PhD in Epidemiology on value-of-information from validating clinical prediction models and AI Our goal: Develop value
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recommend that you apply early as the advert may be removed before the deadline. This fully funded PhD position, offers an exciting opportunity to develop and optimize multiscale models for surfactant
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SyMulDaM project involving the development of predictive models to quantify the integrity and durability of a nuclear power plant containment structure., within the mechanical engineering department
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, energetic processes. Approaches are experimental, with a great emphasis on optical methods as well as theoretical, through modeling of reacting flows. The laboratory is thus developing experimental diagnostic
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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by understanding how cancer risk prediction models are influenced by missing and incomplete symptom data recorded in electronic health records (EHRs) and developing methods to address any issues
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-based predictive models. The successful candidate will develop a highly valuable combination of advanced simulation, AI, and experimental validation skills. On the computational side, they will gain
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systems. Predictive understanding of these interactions is essential for rational formulation design, but reliable theoretical models for these systems remain underdeveloped. This PhD project will develop
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 10 days ago
geometric transformer model to predict protein binding interfaces in flexible and disordered regions. Cell Systems, 10.1016/j.cels.2025.101454 The PhD candidate will: Curate and analyze large-scale datasets
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to taxonomic and ecological groupings. The development and exploration of models that integrate biological understanding with atmospheric dispersion models to predict spatio-temporal spread of wind-dispersed