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required for explanation. This PhD project will focus on the neurosymbolic intersection by treating LLMs as conjecture machines that generate candidate explanations for complex problems, and by developing
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research is essential, and experience working with electronic health records, microbiology, or machine learning would be very welcome. Applications from candidates who do not fulfil the essential criteria
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automated business processes workflows. Situated at the intersection of digital innovation and the built environment, the successful candidate will get to apply their machine learning and software engineering
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scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and
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), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a PhD (or be near completion) in a field such as AI, computer science, signal
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scales, lubrication and wetting, biophysics as well as machine learning or metamaterials. For experimentalists, we particularly interested in applicants aligned with our existing research infrastructure
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. We are now looking for: Three (3) Doctoral Researchers (PhD students) in Machine-Learning-Driven Atomistic Simulations The Data-driven Atomistic Simulation (DAS) group, led by Prof. Miguel Caro
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning
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SMDR. About you To be successful in this role you will be able to demonstrate the following: A PhD in a relevant subject area. Extensive lab experience, preferably with catalytic reaction systems. Prior
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Applicants are invited for the posts of Research Associate or Research Fellow in Machine Learning to work with AI Researchers in the Centre for AI Fundamentals at the University of Manchester. You