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UNIVERSIDAD CATÓLICA DE MURCIA - FUNDACIÓN UNIVERSITARIA SAN ANTONIO DE MURCIA | Spain | about 20 hours ago
Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you passionate about Artificial Intelligence, Machine Learning
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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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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
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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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PI & HOSTING GROUP The direct responsible persons for this post are Dr. Joost van de Weijer and Bogdan Raducanu, members of the Learning and Machine Perception (LAMP) team at the CVC. For more
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combines synthetic biology, machine learning, and biosensor and pathway dynamic regulation design to produce next-generation bio-based chemicals. The Computational Synthetic Biology group (CSBG), led by Dr
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processing (computer vision & machine learning) Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research FieldEngineering » OtherEducation LevelMaster Degree
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of multimedia datasets (voice, text, etc.). Development of predictive models for cognitive impairment and Parkinson's disease using signal processing and machine learning techniques. Development and debugging
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FOR DRAWING UP OF PREDOCTORAL CONTRACTS FOR THE TRAINING OF DOCTORAL STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119865 Development of machine-learning and graph-based models
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STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119977 Development of mathematical and machine-learning algorithms to support an intelligent, integrated system for biosafety