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simulation, including O/D modeling, multimodal network modeling, agent-based or behavioral modeling Large-scale computing, cloud-native analytics workflows, and data engineering for mobility platforms AI/ML
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topics such as statistics, high performance programming, machine learning and using data to constrain cosmological models. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs
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fibroblast identity and function within liver tumours and how these cells shape anti-tumour immune responses. The student will use in vivo cancer models, spatial tissue analysis and immunological profiling
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, graph neural networks, physics-informed ML) to approximate PF results Train models using simulation results generated from conventional power flow solvers Evaluate AI-based approximators in terms
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for seasonal prediction using hybrid physics-machine learning models in R&D item Research on Seasonal Meteorological and Oceanographic Forecast Simulator under Development of Integrated Simulation Platform
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numerical models to improve the simulation of complex multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid
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contributions in one or more of the following key areas: computational modeling of chemical systems, AI-driven materials discovery/design, robotics for chemical synthesis, machine learning applications in
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-world datasets. The core responsibility is to build and validate federated causal inference algorithms through simulations and live demonstrations. Key Responsibilities Participate in and manage the
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Maudsley Centre for Children and Young People: https://www.kcl.ac.uk/research/pears-maudsley-centre which will offer ample of opportunity to collaborate with academics, clinicians and lived experience
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. The research will investigate how phase distribution and flow velocities influence fluid–structure interaction (FSI) and will focus on developing novel sub-models for coupled CFD–FEM simulations. Your tasks