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, or similar) will be valued; 9) Experience in machine learning techniques applied to materials science or process engineering (regression, classification, optimization, predictive models) will be valued; 10
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Your Job: We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
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reports to develop computational models that predict identification reliability. They will learn to design interpretable, legally robust AI systems, including attention-based deep learning models and
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applied in particular to the modeling of 3D-printed concrete at the Navier laboratory, to better predict complex phenomena such as material curing and crack formation. Where to apply E-mail jeremy.bleyer
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-edge in silico, in vitro, and in vivo technologies to understand, predict and treat thrombosis? This is your chance!! Our goal: Develop multi-level thrombosis risk prediction models by integrating
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modelling predictions. Experience or a strong interest in scientific programming and machine-learning-assisted data analysis for materials modelling is an advantage. PhD Position 2 – Coarse-Grained and
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Keen to push the frontiers of multiphase reactor modeling and accelerate the scale-up of emerging net-zero technologies? Join us at the Department of Chemistry and Chemical Engineering! About us Our
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efficiency and lifetime predictions under realistic operating conditions. Validating the developed models using experimental data from drivetrain test benches equipped with load, temperature, vibration, and
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factors such as prediction of plant growth, water pollution, and environmental biodiversity loss. The approach seeks to create robust, explainable models that reflect domain-specific insights, advancing
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biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run high-performance numerical experiments