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ultimately create predictive tools for optimisation and control. About you The ideal candidate will hold a PhD in a relevant discipline (or be near completion) and possess a strong numerical background with
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-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling techniques, including novel AI architectures, for the analysis of complex traits and precision prediction
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University of Stavanger invites applicants for a PhD Fellowship in in molecular modelling and machine learning for improved subsurface utilization, at the Faculty of Science and Technology, Department
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application of mechanistic and hybrid digital twin models for chromatography and tangential flow filtration (TFF) operations, including prediction of binding capacity, multicomponent adsorption, and fouling
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-time PAT data streams to enable predictive monitoring, dynamic process adjustments, and improved control strategies. Working closely with AstraZeneca, the postholder will apply these digital twins and
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model by integrating the newly developed approaches into the numerical program developed in the ‘OceanCoupling ’ project. We would like the successful applicant to start in the first quarter of 2026
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methods to predict these needs individually at the time of stroke diagnosis. This PhD project aims to develop predictive models based on artificial intelligence (AI) that estimate a patient’s brain health
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(BI) aims to develop computational models and calculation tools capable of predicting the structural behaviour of elements produced by accelerated carbonation. The work will integrate experimental data
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force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Employment as PhD Fellow is regulated in Regulations
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to identify early phenotypic markers of efficacy and resistance and to discover predictive biomarkers for off-label therapies. To our knowledge, this will be the first large-scale application of Cell Painting