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Field
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centred on standalone introductory courses in Python or R. Its defining task is to renew how programming and computational practice are taught as agentic coding rapidly changes how computational research is
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generally. We tackle problems in single and multi-agent systems operating in uncertain environments and around humans. We typically work with Markov decision processes (MDPs), and are interested in both
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& Responsibilities Implement, operate, and continuously improve a multi-agent AI system that plans biological research and translates high-level research objectives into executable experimental workflows. Integrate AI
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field. Hands-on experience in designing and implementing software systems involving multi-agent frameworks. Familiarity with immersive platform development (e.g., Roblox, Minecraft), including scripting
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code Developing agentic AI workflows for specification autoformalization, proof generation, and proof repair Build a strong network in the fields of formal verification, systems, and ML infrastructure
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computer-use (“Claw-like”) agents, tool use, and multi-step reasoning. Knowledge of access-control concepts, least-privilege design, and runtime policy enforcement; RL experience is an advantage. Hands
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). Hands-on experience with game AI agents and/or GUI agents such as Mineflayer, Unity ML-Agents, or similar. Solid expertise in computer vision techniques, transformer architectures, and multi-modal
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-assisted decision support, and multi-agent systems. Experience in applying AI and automation technologies to improve business processes, service delivery and operational efficiency. Strong knowledge of Excel
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physical models of insect mechanical structures using a variety of materials such as textiles, 3D printed components, cardboard and wood Robust understanding of the multi-scale systems enabling insect
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-year plans. Experience with project implementation and management, including system deployments. Proven client service excellence Demonstrated ability to act as a change agent, driving process