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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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and autonomous systems, with an emphasis on autonomy in robotics and autonomous vehicles. This position will assist research teams in developing, implementing, and evaluating AI-based approaches
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and Correction of Industrial Design Flaws – Towards Fail-Safe Industries) is a Business Finland funded research project with the objective to develop a new type of computer-aided design platform
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by integrating large-scale single-cell foundation models with structured biological knowledge encoded in genomic graphs. The project will also deliver efficient algorithms to train these models under
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verification of security protocols and systems, cryptographic algorithms and protocols, analysis of the security of cryptographic algorithms, post-quantum security. More details can be found on the CSE Web site
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. You can continue your career journey with us! The Slomka Laboratory focuses on developing innovative methods for fully automated analysis of nuclear cardiology data using novel algorithms and machine
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nuclear and particle physics research leveraging machine learning and AI for data analysis and detector development, as well as exploratory work in quantum algorithms, depending on background and interests
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of Coimbra III- Scientific supervision/coordination of the grant: Rui Paulo Pinto da Rocha IV - Work Plan / Goals to be achieved: 1. Development of algorithms for swarm robotics and human–swarm interaction 2