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project focuses on developing ultra-reliable spatiotemporal (4D) predictions using trustworthy, distributed AI-driven intelligence deployed across heterogeneous aerial nodes. To achieve this, an aerial
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simulations on the Aurora supercomputer, using AMReX (https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry-aware refinement strategies that go
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, biomedical/clinical informatics, and/or signal processing, to develop and evaluate algorithms to interpret continuous bedside monitoring data, integrating additional clinical data such as electronic medical
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the radiosensitivity of patients. 3) Expansion of the algorithms developed to include fractionation as an optimization variable. Legislation framework: Research Fellowship Holder Statute, in accordance with Law 40/2004
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robotics Goal-driven agentic AI Autonomous medical imaging Design of AI-enhanced medical devices Machine learning models and algorithms for medical signal processing Embedded AI Privacy-aware AI Foundations
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computing, networked systems, and beyond. The work will range from theoretical and algorithmic development of distributed protocols and coordination mechanisms, through the design and implementation
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to ensure that the developed notations and algorithms address the companies’ needs. More about the related project can be found here: https://innovationsfonden.dk/da/news-article/ai-skal-forudsige-og-forklare
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Your Job: Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep
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will be involved in the Trusted Autonomous and Self-Adaptive Systems for Defence (SAACD) project. This project aims to overhaul the engineering and development of this type of complex system. A SAACD can
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knowledge and fosters the development of highly skilled researchers and professionals. Our research focuses on material properties and manufacturing processes for mainly metallic components, specifically cast