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in machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to adel.bibi@eng.ox.ac.uk For more information about working at
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together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is
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) in machine learning or a closely related field you should possess sufficient specialist knowledge in the discipline to work within established research programmes and have an ability to manage own
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on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related discispline. You
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Machine Learning, Statistics, Computer Science or closely related discipline. They will demonstrate an ability to publish, including the ability to produce high-quality academic writing. They will have the
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying
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Aalto University is looking for an Postdoctoral Researcher in Artificial Intelligence / Machine Learning Engineering [Academic Research Software Engineer] to a postdoctoral-level position. The
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capabilities o Demonstrated experience with machine learning and/or statistical modeling o Expertise in handling large-scale, complex datasets with strong data wrangling skills o Strong publication record
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This position is for a Postdoctoral Research Associate on the EPSRC New Investigator Award `Accelerating Laboratory Automation Through Learning Tool Morphology For Robotic Chemists¿, a project
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To undertake research on the evolution of prokaryotic pangenomes using machine learning and AI approaches. The work will involve the analysis of large prokaryotic genome datasets, the development