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Field
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-funded AI research group “Fusing Deep Learning and Statistics towards Understanding Structured Biomedical Data (DeSBi)” development of deep neural networks and machine learning algorithms for the analysis
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research team. Key research areas include: Development of low-carbon materials and tunable thermal energy storage materials integrated with smart sensors and advanced algorithms Creation of Digital Twins
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, and computer graphics, to identify existing research, methodologies, and advancements. Designing and developing innovative algorithms, models, and techniques for solving complex problems in computer
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oppdatert 24.03.2026 Hva er en cookie? En cookie er en liten datafil som lagres på datamaskinen, nettbrettet eller mobiltelefonen din. En cookie er ikke et program som kan inneholde skadelige programmer eller
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that are relevant to industry demands while working on research projects in SIT. The Future Ship and System Design (FSSD) programme aims to develop strategic and innovative design capabilities for the maritime
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offering a graduate research fellowship at the Combat Capabilities Development Command – Chemical Biological Center (DEVCOM-CBC). As a research fellow, you will join a community of scientists to support our
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his/her scientific training. In particular, it is intended to develop and apply remote sensing methodologies and tools for the detection and modelling of habitats, as well as the extraction
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 months ago
& PEWER_Level B_Research Fellow_SOCO_CSS_2026.pdf Opportunity to work on a high-impact Defence-funded research project under the ASCA Emerging and Disruptive Technologies program. Develop cutting-edge AI planning
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capacity to process complex simulation data, fine-tuning its interpretation algorithms, and ensuring that gap-filling recommendations are both biologically plausible and supported by external resources
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Responsibilities: Conduct programming and software development for graph data management. Design and implement machine learning models for optimizing graph data management. Conduct experiments and evaluations