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Field
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) department at Telecom Paris. Reinforcement learning (RL) has emerged as a useful paradigm for training agents to perform complex tasks. Model-based RL (MBRL), in particular, promises greater sample efficiency
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researcher, with strong analytical and computational skills, with knowledge of one or all of the following: Dynamic Transport Modelling Demand Estimation and Forecasting Agent-based Modelling and Simulation
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Perturb-seq, with our integrative analyses often employing computational approaches such as agent-based modeling to deconstruct gene-regulatory networks and predict system behaviors. As a postdoctoral
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agents for X-ray spectroscopy by integrating large language models (LLMs) with physics-aware spectroscopy workflows. The researcher will work closely with a multidisciplinary team of X-ray physicists and
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-informed neural networks (PINNs) for integrating mechanistic constraints into ML frameworks, and creating LLM-based agents to assist with mechanistic model construction and knowledge curation. Track B
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Forecasting Agent-based Modelling and Simulation Data-driven Modelling Transportation Planning and Network Design Your profile Hold a PhD in applied mathematics or engineering Strong analytical and numerical
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models Disseminate research through publications, presentations, and open-source contribution Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data
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, PyTorch, JAX etc. Experience with modern AI concepts such as large language models (LLMs), vision-language models (VLMs), model context protocol (MCPs), and the development of agentic AI tools. Skill in
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) 4+ years’ experience in Matlab™/C++/Python 2+ years’ experience with acoustic modeling software (k-Wave, FOCUS, Field II) 2_ years’ formulating and characterizing nanoparticles or contrast agents
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individual synovial membrane and osteochondral unit-on-chip models as well as a their combination in the joint-on-chip to test established and new disease modifying agents to validate their use in drug