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Field
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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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either, is challenging our standard models of planet formation. Our goal is to predict and reproduce the architecture of these exoplanetary systems and the exoplanet properties, including composition
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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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. Model parasitics (leakage inductance, interwinding capacitance) for accurate simulation and EMI prediction. Analyze core and copper losses and apply thermal management strategies for high power density
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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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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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(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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Biodiversity, Genetics and Evolution Work plan: The fellowship holder will focus on developing and applying innovative ecological modelling approaches to improve predictions of species distributions under global
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the strengths and weaknesses of different solutions; 2. Integrate sensors to monitor important parameters in real time, enabling predictive rather than reactive control and preventing component failure; 3