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Field
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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14 Jul 2026 Job Information Organisation/Company CNRS Department Centre de Recherche en Informatique de Lens Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage
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authenticity of visual media and provide understandable evidence for model decisions. The candidate will investigate how general-purpose pretrained visual and multimodal representations can be adapted
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of Advanced Computer Science (LIACS). As a team, we develop cutting-edge techniques for advanced computational imaging systems, combining expertise from Mathematics (Inverse Problems), Computer Science (Machine
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-fidelity modeling approaches combining simulations of different fidelity levels, ranging from RANS calculations to high-fidelity LES, possibly associated with various levels of geometric or physical
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Description The main tasks to be carried out by the selected candidate will be the following: ● Develop new methods for the uncertainty quantification of non-linear statistical models using the 'online
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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computational and energy costs of LLMs, this work will focus on smaller, domain-specific models. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR8254-SYLDES-026/Default.aspx Requirements
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accurate modelling and understanding of damage at this length scale can help inform life assessment at the component scale. Current models are able to capture grain-averaged mechanical behaviour effectively
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focus We welcome candidates with a strong interest in developmental biology, organoid technology, and disease modelling. This position is part of the EU-funded Marie Curie doctoral network (VISI-ON-BRAIN