4,787 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions
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datasets to improve the understanding and prevention of cardiovascular and cardiometabolic diseases. You will have the opportunity to conduct data-driven phenotyping, association and causality analyses, and
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platform. • Collect and manage data via a server. • Develop robust backend tools for structuring and interconnecting data, • Integrate artificial intelligence models for analysis, interpretation
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support the neutronics and thermal-hydraulics analysis. Major Duties/Responsibilities: Design, develop, optimize and analyze reactor models using world-class modeling and simulation codes (SCALE, VERA
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Martina Schroeder’s research group at Maynooth University and her national and international collaborators on a HRB-funded project that aims to generate a hiPSC-based preclinical model for DDX3X syndrome, a
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of dissipation in magnonic systems. Develop theoretical models to describe and optimize the control of magnon dissipation via feedback. Collaborate with the experimental team to validate these models and propose
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at scale. By integrating AI-driven image analysis with molecular and clinical data, the work seeks to identify novel disease-associated features and translate these into clinically deployable models
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will be critical to work closely with the other 3DxN teams, with potential opportunities to utilise and develop skills in AI inference models, data integration, 3D image reconstruction or signal
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field, has hands‑on experience in autoimmune disease models or adaptive immune modulation, and is comfortable integrating materials science with biologic assays. Qualified applicants must have experience
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tensors and Taylor models as tools to map neural systems onto mathematically well-understood objects. Pioneering the field, the ACT has developed several innovations, including deep learning for guidance
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across heterogeneous data types such as clinical, imaging, omics, text, and experimental data. The work will include developing approaches for continual model improvement, adaptive federated training