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and accelerate the development of more high-performing PNSEs. The ultimate goal of the project is to develop, implement, and validate novel deep-learning models for molecular dynamics and coarse-grained
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fair access to opportunities (employment, healthcare services, education) and mitigating spatial inequalities; - develop (deep) learning models for spatial structures and dynamic graphs to support the
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
, financed by EU and national funds through FCT/MCTES (PIDDAC Workplan: Machine learning algorithms, in particular deep learning ones, suffer from the phenomena of catastrophic forgetting, which hampers
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on artificial intelligence techniques, namely machine learning and deep learning; (3) analysing mathematical models applicable to renewable energy generation technologies and electrical energy storage systems; (4
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(pore sizes below 100 nm) structures. Excellent scintillation properties, particularly a high radioluminescence light yield. Maximal transparency to ensure that luminescence flashes can be detected deep
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factors: Prior experience in developing algorithms for biomedical image processing (especially aligned with the research group's areas) and machine learning/deep learning techniques. Prior knowledge of data
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with the centre’s user partner Kongsberg Satellite Services (KSAT). We are therefore seeking someone with a strong interest and competence in deep learning. Working environment: The project will be done
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compression, event-based or neuromorphic vision, signal processing, machine learning or deep learning for visual data. - Motivation for research and scientific dissemination. - Good communication skills in
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application development experience Maritime operational knowledge & experiences. Large Language Model-based application development. Deep Learning techniques, Data Engineering, and Semantic Technologies Open
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or a Scandinavian language is also beneficial. The following are considered beneficial: solid theoretical background in robot perception and navigation deep foundation in modern machine learning solid