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to crack initiation and assess the influence of the microstructure of Almelec alloys. The results will also be used to improve a predictive FEM model (Abaqus/Python) simulating crack initiation and
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state‑of‑the‑art structure prediction and design frameworks, training/fine‑tuning models, and running scalable computational campaigns. Key responsibilities Design and execute in silico protein and
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at Tilburg University, in collaboration with the Faculty of Military Sciences and the Joint Sigint Cyber Unit, invites applications for a fully funded postdoctoral position focusing on predictive modelling
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well as interpretability of scientific foundation models. The rush to build foundation models has led to the development of large machine learning models in Astrophysics, fluid dynamics, biology, weather prediction, solar
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Description About the position We are looking for a motivated PhD candidate to join a multidisciplinary research team working on predictive modeling of clinical outcomes after Deep Brain Stimulation (DBS) in
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unit dedicated to advancing the understanding, monitoring, and predictive modelling of modern engineering structures. Research within the department on Structural Health Monitoring (SHM), non-destructive
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testing data Development of machine learning models for battery health assessment and remaining useful life prediction Job Requirements: PhD degree in Electrical Engineering or related subjects. Expert
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A postdoc position is available immediately in the laboratory of Dr. Daniel Blair which focuses on leveraging high-throughput chemical synthesis and MS/MS analysis to create predictive models
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screening to a predictive, rational design of absorption media (WP1) and to validate their efficiency in VOC capture, with a specific focus on emissions from the semiconductor industry. (WP2). The project
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sustainable fluorination reactions. Under the supervision of Dr. Chris Ewels, a CNRS Research Director and expert in DFT modeling of nanocarbon materials, the postdoc will lead Work Package 2 (WP2), which aims