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comment: The Oxford Internet Institute is a leading centre for research into the governance of emerging technologies. Applications are invited from highly qualified individuals for the position of Postdoctoral Researcher. This fixed term position is available immediately for a duration of 14 months (or to funding end date of 1 April 2025,) with a possibility of renewal thereafter, subject to funding availability. The position is located in the centre of Oxford. They will work with Professor Brent Mittelstadt (Associate Professor and Director of Research) and Professor Chris Russell on bias, fairness, interpretability, and trustworthiness in medical AI applications involving large language models (LLM), deep learning, and other state-of-the-art machine learning methods. The post will be part of the ‘Advancing machine learning to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis’ project, funded by UKRI, which will advance ML to achieve real- world early detection and personalised disease outcome prediction of inflammatory arthritis. The position requires a candidate with the ability to coordinate and deliver a work package focused on technical aspects of bias, fairness, and trustworthiness in machine learning for medicine, and publish results in peer- reviewed international academic journals and conferences. The post-holder will be responsible for coordinating and leading work to measure trustworthiness and mitigate bias in LLMs, deep learning (including convolutional neural networks), semi-structured healthcare data, and unstructured GP records. They will also collaborate with researchers leading work to develop novel interpretability methods in medical AI. Excellent technical, analytic, and writing skills, and expertise in machine learning, natural language processing, computer vision, human-computer interaction, or similar relevant areas are required. A record of publication in international peer-reviewed journals and …
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comment: The Oxford Internet Institute is a leading centre for research into the governance of emerging technologies. Applications are invited from highly qualified individuals for the position of Postdoctoral Researcher. This fixed term position is available immediately for a duration of 14 months (or to funding end date of 1 April 2025,) with a possibility of renewal thereafter, subject to funding availability. The position is located in the centre of Oxford. They will work with Professor Brent Mittelstadt (Associate Professor and Director of Research) and Professor Chris Russell on bias, fairness, interpretability, and trustworthiness in medical AI applications involving large language models (LLM), deep learning, and other state-of-the-art machine learning methods. The post will be part of the ‘Advancing machine learning to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis’ project, funded by UKRI, which will advance ML to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis. The position requires a candidate with the ability to coordinate and deliver a work package focused on technical aspects of bias, fairness, and trustworthiness in machine learning for medicine, and publish results in peer-reviewed international academic journals and conferences. The post-holder will be responsible for coordinating and leading work to measure trustworthiness and mitigate bias in LLMs, deep learning (including convolutional neural networks), semi-structured healthcare data, and unstructured GP records. They will also collaborate with researchers leading work to develop novel interpretability methods in medical AI. Excellent technical, analytic, and writing skills, and expertise in machine learning, natural language processing, computer vision, human-computer interaction, or similar relevant areas are required. A record of publication in international peer-reviewed journals and th…
salary: Grade 07S: £36,024 - £44,263 per annum
application opening date: 2023-10-06T15:00:00+01:00
application closing date: 2023-11-08T12:00:00+00:00
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value: Job Description _________________________________________________________________________ Job title Postdoctoral Researcher Division Social Sciences Department Oxford Internet Institute Location 1 St Giles – OX1 3JS Grade and salary Grade 7; salary £36,024 - £44,263 per annum Hours Full-time Contract type Fixed-term for 14 months or to funding end date of 1 April 2025 Vacancy reference 168496 Research topic Fairness, bias, interpretability, and trustworthiness in medical AI Principal Investigator / supervisor Professor Brent Mittelstadt (Associate Professor) Funding partners UKRI Overview of the role Fairness, accountability, transparency, and ethics in machine learning (ML) and artificial intelligence (AI) are now major areas of academic research, development, and policy. Many technical and organisational tools, such as computational methods to explain model outputs, statistical tests for fairness, or algorithmic impact assessments, have been developed to address these challenges. However, the mere existence of such tools is insufficient to ensure trustworthy AI. Little is known about the efficacy of different tools intended to make AI systems trustworthy and accountable, how their utility varies across different use cases and domains, how they can be used incorrectly or maliciously, or how best to motivate ‘buy-in’ among responsible communities of practice. As a result, it remains unclear whether such technical and organisational tools will actually produce machine learning and AI systems that remain trustworthy and accountable over time. The Governance of Emerging Technologies (GET) research programme addresses this gap in knowledge by undertaking applied research and developing tools and tests to evaluate the efficacy of existing AI accountability tools. The post-holder will lead a work package examining bias, fairness, interpretability, and trustworthiness in medical AI applications involving large language models (LLM), deep learning, and other state-of-the-ar…
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