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or predictive modelling, edge AI, AI for biomaterials formulation, processing and manufacturing optimization. Wearable devices – wearable physiological sensors, smart textiles, soft robotics, and exoskeletons
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the Telemark Canal , focusing on digital twin-based preparedness modelling for cultural heritage infrastructure. The primary objective of the position is to complete a doctoral education leading to a PhD degree
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the intersection of explainable artificial intelligence (XAI) and causal inference. Our goal is to develop AI systems that are causally understandable, such that predictions can be interpreted in terms of underlying
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: leverage native capabilities, including Virtual Agent, Predictive Intelligence, and GenAI, to enhance self-service and drive significant operational efficiency. Execute Strategic Project Leadership: Drive
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, robustness, and generalizability of the proposed models. The research aims to advance intelligent and reliable prediction, diagnosis, monitoring, and performance assessment of metal additive manufacturing
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genomics. DNA delivery. Predictive models of DNA sequence to function, at the scale of genes and genomes. Human health applications and delivery mechanisms. We will consider applications on a rolling basis
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includes the following tasks: Develop computer-aided design software for modular construction of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models
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-guided) Evolutionary trajectory analysis and fitness landscape modeling Integration of predictive algorithms with experimental iteration cycles High-throughput screening and selection platform development
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predictions of the models. Placement: La Rochelle Université, laboratory LaSIE, UMR 7356 CNRS Recruitment: fixed-term contract 12 months Working hours: full time Contract starting: Spring 2026 Salary: According
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predictive models to quantify shrubification risk; and (iii) investigate the implications of shrubification for water markets and policy. Teaching & Engagement: Contribute to climate fluency and sustainability