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Field
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An exciting opportunity has arisen for a talented postdoctoral researcher to join the Oxford Machine to work on a research project in the area of robust, large-scale agent-based modelling. The post
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to understand these dynamics. This project proposes a novel pipeline of ideas to generate tools and techniques to simulate HIV infection dynamics using a multiscale agent-based modelling technique (cells, viruses
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networks of LLM-based agents to solve complex problems has shown great potential, for example in DeepMind’s Co-Scientist System. Against this background, the goal of this project is to reframe the field
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Python-based agent pipelines (e.g., LangGraph) and integrate diverse large language models (LLMs) and multimodal vision models. Additionally, you'll build and maintain data flows specifically tailored
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or mechanical engineering well above average and you should have experience in agent-based modelling and simulation. Fundamental knowledge of the key disciplines in aerospace engineering and especially in airport
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electrolysis. As an MSc student, you will design and implement a suite of AI “agents” (autoencoders, statistical models, LSTMs and LLM-based rule engines) that process historical and live sensor data (voltage
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model development, multi-agent decision-making in autonomous systems, advanced perception, and cognitive human-machine interface that support the human users. Candidates will have the opportunity
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data of emotional states and personality metrics. Modeling efforts will focus on agent-based approaches, which may include both simulation studies and data-driven modeling approaches to understand
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-Based Generative Models: How can we fundamentally redesign generation processes for superior efficiency, controllability, and quality? We are exploring diffusion models, flow-matching, and other parallel
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, a spatially-explicit agent-based model of land use change in Great Britain. This will be coupled with a system dynamics model of macro-scale drivers of land system change. Working with colleagues from