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The Novo Nordisk Foundation Quantum Computing Programme (NQCP) at the Niels Bohr Institute, University of Copenhagen, is seeking a Quantum Systems Modeling & AI Engineer or Academic Research
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programme in the field of cardiovascular computational modelling. Under the supervision of the project’s researchers, they will carry out the following tasks: Implementation and validation of computational
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LMPS - CNRS - CentraleSupélec - ENS Paris-Saclay | Gif sur Yvette, le de France | France | about 13 hours ago
. This model is implemented within a dedicated, object-oriented code, based on the finite element method and developed in a MATLAB environment. The work program of this thesis is structured around 2 main axes
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, seeks to recruit a junior research scientist to develop AI-enabled healthcare applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based
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programme in the field of cardiovascular computational modelling. Under the supervision of the project’s researchers, they will carry out the following tasks: Development of fluid–electro–bio-mechanical
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algorithm development and system performance. Technical Focus Areas Computer Vision and Model Development: Design and train deep learning models for insect classification and morphological recognition
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) and a global ocean model (NEMO). The selected candidate will contribute to the ANR-AIAI project (https://anr-aiai.github.io ). Scientific Context The melting of Antarctic ice shelves by the ocean is a
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http://www.unifi.it Requirements Additional Information Eligibility criteria Master’s degree in Computer Engineering or Master’s degree in Artificial Intelligence Eligible destination country/ies
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the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description ETECNIC is a technology company specialized in
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Deployment Strategies - Model Compression: Investigate techniques such as quantization, pruning, and knowledge distillation to reduce the computational and memory footprint of deep learning models without