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Field
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or adaptive systems reinforcement learning, multi-agent systems, network or graph-based models simulation of complex socio-technical or organisational systems causal inference, econometric analysis, or formal
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pipeline for predicting human pathogenicity based on bacterial genomes, something that has recently been expanded using more novel AI-techniques. There is, however, a need to further expand this to also
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therapeutic agents, using cell-based models as well as patient-derived xenograft models of liver cancer. This position is suitable for a highly motivated self-starter who excels in a dynamic environment
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. You will then develop a procedure to translate these quantified relationships to a predictive agent based model for the investigation of animal movement behaviour under future climate scenarios. We also
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to staff position within a Research Infrastructure? No Offer Description CIATec selects 1 FAPESP Postdoctoral Fellow to work on the development of predictive models and recommendation systems, based
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databases and retrieval-augmented generation (RAG) and integrate agentic AI systems to meet the demands of large-scale fine-tuning and inference. The postdoc will also work on design and implement agentic-AI
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reinforcement learning Experience with high-performance computing, physics-based simulations, and multimodal data workflows Demonstrated ability to train and deploy AI/ML models using simulated and experimental
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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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sparse-regression based techniques to derive interpretable and computationally efficient differential equation models from computationally intensive multi-cellular agent based models (ABMs) of Epstein–Barr
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supervision of PI Calina Copos and will engage in collaborative work with cell and developmental biologists. Expertise in agent-based models, continuum PDE descriptions, dynamical systems, and/or ML-based