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Field
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transplantation using mouse animal models. The research involves understanding the mechanisms underlying resistance/relapse following immunotherapy such as CAR T and developing novel agents for protecting
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from geomatics, spatial analysis, multi-agent modeling and simulation, and laboratory work (soil and surface formation studies; host of the Solsup platform of the EMerode service unit). General
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of computing and healthcare. Methodologies of interest include: Multi-modal learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised
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Researcher to join the group. The research project is concerned with the challenging problem of modeling the complex modern radio environment, where a diverse set of devices and agents share the available
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approaches (e.g. ODE/PDE-Modelling, Agent Based Modelling) Experience in mathematical analysis of data-driven methods Solid experience in programming (R, Matlab, Python, etc.) Competence in working in inter
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Scene Synthesis: Re-design diffusion and NeRF-style models so multiple agents jointly reconstruct a scene. Cooperative Scene Understanding: Re-design semantic segmentation and object-detection models in a
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language processing (NLP), and fine-tuning techniques Familiarity with structured reasoning, chain-of-thought processes, and agent-based systems is beneficial Strong programming skills (preferably Python); experience
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mouse models. * Experience with cell and molecular biology techniques including PCR, western blotting, flow cytometry, cell culture and immunohistochemistry assays. Position Summary: The successful
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. ● Experience with mouse models. ● Experience with cell and molecular biology techniques including PCR, western blotting, flow cytometry, cell culture and immunohistochemistry assays. Position Summary
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. ● Experience with mouse models. ● Experience with cell and molecular biology techniques including PCR, western blotting, flow cytometry, cell culture and immunohistochemistry assays. Position Summary