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design spaces, long-duration simulations, and real-time applications. This PhD project will develop physics-informed deep learning and surrogate modelling approaches to accelerate simulation, uncertainty
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without Nature-based Solutions (NbS) and considering the principal uncertainties associated with data, models, and climate scenarios. The methodology will also be extended to integrate heterogeneous
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Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0247.pdf CALL FOR GRANT APPLICATIONS (AE2026-0247) INESC TEC is now accepting grant applications to award 1 Research Grant (BI) within
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Porto, Portugal and will be published on the website https://cbqf.esb.ucp.pt/. Candidates will be notified by email once this list has been made available. 8. Preliminary Hearing and Final Decision
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of MSc and PhD students and support ongoing research activities within the project Where to apply Website https://www.academictransfer.com/en/jobs/362790/postdoc-climate-adaptation-path… Requirements
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Computer Science, sub-areas of Artificial Intelligence, Serious Games and Medical Image Processing, and supported by the Multiannual Funding of R&D Units of the FCT (UID/5210/2025) Where to apply Website https
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). Computational modeling of complex systems, including physics-based or data-driven approaches. Emerging challenges in modern numerical analysis, such as uncertainty quantification, high-dimensional problems
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inference pipelines for soft biomechanical systems, including differentiable physics engines, to support interpretable analysis, parameter estimation, sensitivity studies and uncertainty quantification. By
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 2 months ago
incorrectly or fail to meet the required qualifications for this competition will be excluded from admission. The jury reserves the right to request any candidate, in case of doubt, to provide supporting
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 months ago
, Australia. The position is offered in connection with the ARC Discovery Project grant “Responsible Statistical Learning: Uncertainty, Fairness and Transparency”. The successful candidate will play a key role