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composites for enhanced durability, performing microstructural analysis and mechanical testing. Topology Optimization & AI Integration: Use AI and machine learning to guide structural and topology optimization
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, development, and training of machine learning and deep learning algorithms. Creation of accurate, robust, and energy-efficient models. Development of systems capable of predicting and making decisions in real
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early detection is a need that needs to be addressed using advanced sensors. The candidate will apply machine learning and IA methods to anticipate the evolution of the discharges. This project aims
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Learning for Foundation Models’, where the aim is to adapt these models to new tasks without forgetting previous knowledge. The precise focus of the project can be defined in collaboration with
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: Modelling of optimization problems mainly related to the indicated line of research, as well as the design, implementation, and validation of algorithms to solve them Where to apply Website https
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Machine Learning A PhD position is available at the Computer Vision Center (CVC) under the supervision of Fernando Vilariño and Paula García . The successful candidate will be enrolled in
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area), interested in biomanufacturing in biofoundries, and have technical skills in computer programming and basic knowledge of mathematical models for systems and/or synthetic biology. A secondment at the Technical
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for experiments using reinforcement learning, Bayesian methods, image analysis and data analysis. Collaborate with interdisciplinary teams, including machine learning experts, device modelling specialist
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. Preliminary exposure to machine/deep learning, statistical modelling or generative AI. Application process: Interested candidates are invited to apply via the PHYNEST online platform by submitting a full CV, a
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work. Availability for a ≥4 month stay abroad. Background in at least one of: AI/machine learning, computational modelling, microscopy, or cell/molecular biology. LanguagesENGLISHLevelGood Additional