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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 2 months ago
to be carried out focus on the daily operational activities of home hospitalization units, with the goal of developing optimization models and algorithms to support the planning of routes, schedules, and
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. About your role: Develop improved physical models of the image formation process in holographic X-ray imaging Design and implement reconstruction algorithms for handling large-scale tomographic data from
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to train tomorrows leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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Your Job: Quantum computers will play a crucial role in the development and optimization of battery materials in the future. In this project, you will develop innovative quantum algorithms
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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through genome sequencing, but these technologies can only read DNA fragments of limited length. We enable biological interpretation of these sequencing data sets by developing algorithms based on graph
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, efficient and context-aware, creating the foundations for the next generation of wearable and augmented reality platforms. The research focuses on developing novel ML methods that learn from limited resources
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reconstruction, and the need to evaluate generated and transmitted data in terms of their relevance and utility for achieving specific objectives. To address these challenges, the project will develop theoretical
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Are you eager to research and develop Visual Analytics techniques for Event Sequence exploration and analysis? Are you curious how to combine and explore the synergy between Visualization and
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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning