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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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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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skills in generative models applied to the design of bioactive peptides, with a strong focus on integrating these methods into the IA4Farma ecosystem. The work will include collecting and preparing peptide
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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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Assistant to support applied research at the intersection of healthcare, artificial intelligence, and data analysis The primary focus of this role will be large language model (LLMs) and other machine
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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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of mathematical derivations — checking proofs, running numerical simulations, implementing models in Python or Mathematica, researching the relevant literature. You do not need to be an expert in all areas the QBF
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and the supporting evidence of eligibility for point 7 should be sent exclusively via the following weblink: https://posao.pmf.hr/opening-login?openingUUID=e5bf9166-6cf3-4b35-83e8-d95ad1522c45 Where
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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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. 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