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27th April 2026 Languages English English English The Department of Structural Engineering has a vacancy for Two PhD positions in “Micromechanics-based modelling of ductile failure in high-strength
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-learning (ML)- driven and physics-based computational workflows to screen large molecular libraries, predict key electrochemical and physicochemical properties, and deliver ranked shortlists of high[1
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models trained on the Year 1 dataset to predict promising compositions. The third year focuses on application and physical metallurgy insights, where the student will apply the refined methodology to a
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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/trait data) Conducting statistical modeling, feature selection, and predictive analytics for forest health, resilience, and biomass estimation Supporting data preprocessing, cleaning, normalization, and
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techniques for analyzing data collected from hardware sensors installed on electrospindles and the development of models for calculating the spindle’s remaining useful life. The contractor will explore topics
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education setting and a working knowledge of higher education regulations and policies Experience in advanced data analysis, predictive modeling, and leveraging data to drive strategic decision-making
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research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. The Research Engineer (Modelling and
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, wearable devices, and human movement. The ideal candidate will demonstrate experience conducting interdisciplinary research with healthy or clinical populations that applies AI models to predict relevant
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skills in generative models applied to the design of bioactive peptides, with a strong focus on integrating these methods into the IA4Farma ecosystem. The work will include collecting and preparing peptide