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, and machine learning models for functional genomics research in mycobacteria. Responsibilities Responsibilities include: Develop and maintain Django-based web applications and databases for sharing
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-based techniques (e.g., deep neural networks) will be used to automatically learn the system dynamics and the modelling errors, as well as to obtain an automatic tuning of the cost parameters/constraints
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behaviours of multi-agent systems in response to changing internal states and external environmental conditions. Both traditional model-based approaches and modern learning-based control techniques will be
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Université Paris-Saclay GS Life Sciences and Health | Fontenay aux Roses, le de France | France | 4 days ago
. We have also developed highly accurate structural models of control protein complexes in association with Rad51 filaments. We will use a multidisciplinary approach based on genetics, molecular biology
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., hydrogels, polymers, structured tissues), with applications in biomedical engineering, soft robotics, and more broadly in adaptive multiphysics systems. The developed models will be physically based and will
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for part-time employment. Starting date: 27.03.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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Your Job: In an interdisciplinary team, you will implement approaches for the automated, large-scale availability and integration of energy system data and models, applying data science methods
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. The doctoral candidate will engage in interdisciplinary research, modeling threat actors, and developing AI-based monitoring tools. Responsibilities include publishing in top-tier journals/conferences and
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theory, modeling, and AI-assisted optimization activities within the consortium. Reporting and dissemination of the results. Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=1074
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DOE L-level security clearance Qualifications We Desire: Demonstrated experience with model-based reinforcement learning for real-time control applications Demonstrated experience with convolutional