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Field
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nonprofits to use machine learning and data science to modernize the public sector. We are an interdisciplinary team of data scientists, social scientists, engineers, and lawyers who are passionate about
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development plan, specifying career goals and the competencies that the PhD fellow should acquire, no later than one month after commencement of the fellowship period. The department is responsible for ensuring
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necessary lead relevant meetings. To undertake any other duties relevant to the programme of research. Job Requirements: PhD degree in Computer Engineering, Computer Science, Electronics Engineering or
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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Partnership between UCL and AstraZeneca and to work as part of a cross-disciplinary team across both sites (London and Cambridge). This post is focused on the use of machine learning models of protein
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verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research independence, the capacity to support junior team members, and strong communication
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of Medicine We focus broadly on quantitative and machine learning techniques in multiple modalities of medical imaging (e.g. fundoscopy images, OCT scans, MRI, CT, X-ray and digital pathology). We bridge
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to the project. Job Requirements: PhD degree in Computer Science, Electrical and Electronic Engineering, or related field. Min 3 years of relevant experience in computer vision, artificial intelligence, etc
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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to possess robust empirical research knowledge within the realm of political science and be proficient in conducting quantitative analyses. Experience with large language models, machine learning, and/or