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Framework Programme? Not funded by a EU programme Reference Number 173095 Is the Job related to staff position within a Research Infrastructure? No Offer Description CASTRO aims to develop wireless signal
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 26 days ago
scientists developing computational simulations in various fields will be encouraged, in particular with the Gysela code, which is part of the ITER project. The project will first focus on dimensionality
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approach based on Deep Learning algorithms will be developed and implemented to obtain additional information by coupling the recorded data. Furthermore, the increase in acquisition rates of measurement
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 26 days ago
be developed at VisLab of Institute for Systems and Robotics, under the scientific supervision of Prof. Catarina Barata. Website for additional job details https://ist-id.pt/concursos/bolsas/ Work
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description languages. Develop and optimize EDA workflows for processor and accelerator design, verification, and physical implementation using open-source tools. Explore architecture-algorithm co-design for
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The Computational Protein Engineering (CPE) group at The Novo Nordisk Foundation Biotechnology Research Institute for the Green Transiation (BRIGHT) is developing novel methods to engineer proteins
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imaging (MRI). The Computational Biomedical Imaging Group (CBIG) pursues research on the development of new algorithms for the reconstruction and post-processing of medical and biological images. Active
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will explore how semantic digital twins can automate the generation of control-oriented models and how LLMs can facilitate natural language interaction with complex building management. The developed
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typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance