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Position: PhD Position using Machine Learning for Adverse Event Prediction in Cancer Therapy
Institution: University of Cambridge
Location: Cambridge, Cambridgeshire, United Kingdom
Duties: This project aims to use artificial intelligence to identify clinical and imaging features that can predict important ICPI- toxicities such as pneumonitis (inflammation of the lung) and hepatitis (inflammation of the liver) from standard of care CT scans performed either at baseline (pre- treatment) or during ICPI treatment. Imaging features may be used to identify toxicity earlier (before symptoms), enabling clinicians to better manage toxicities at an earlier stage and/or to personalise follow up in patients at higher risk of ICPI-toxicity, and to optimise ICPI treatment
Requirements: The candidate must qualify for home (UK) tuition fees due to funding constraints. The candidate must hold (or be predicted to achieve) the equivalent of a first-class or upper-second undergraduate degree in biomedicine, Mathematics, computer science, or a general natural science. Experience with computer languages and code writing are highly relevant for the project and an aptitude for computer programming is essential. An interest in applying technology to healthcare settings is important. The candidate must be self-motivated, a good problem solver, a great communicator, and be able to work cohesively within a multi-disciplinary team
   
Text: University of Cambridge About us Contact us Job Opportunities Jobs Jobs Applying for a job My applications Right to work What we can offer Living in Cambridge Temporary work Work experience Volunteering Academic Research Professorships/Directorships Academic-related Assistant staff Studentships Joint AstraZeneca-University of Cambridge PhD Position using Machine Learning for Adverse Event Prediction in Cancer Therapy Applications are invited for an AstraZeneca funded non-clinical PhD awarded to the University of Cambridge to be led by Dr Michael Roberts at the Department of Applied Mathematics and Theoretical Physics (DAMTP) and Dr Sarah Welsh at the Department of Oncology. The successful applicant will spend time at the AstraZeneca R&D site in Cambridge, as well as the DAMTP and Department of Oncology at The University of Cambridge, and will be fully embedded into all teams to ensure the necessary complementary skillsets are developed during the PhD. Background to the project: Immune checkpoint inhibitors (ICPIs) including anti-PD(L)1 and anti-CTLA4 monoclonal antibodies have revolutionised cancer treatment, improving survival in a wide range of solid malignancies. However, up to 60% of patients will not benefit from treatment with these agents, yet up to 95% experience toxicity, with a significant number requiring long term immunosuppression to manage immune-related toxicities. There is an urgent clinical need to improve patient selection and identify those at risk of toxicity to improve patient outcomes. This project aims to use artificial intelligence to identify clinical and imaging features that can predict important ICPI- toxicities such as pneumonitis (inflammation of the lung) and hepatitis (inflammation of the liver) from standard of care CT scans performed either at baseline (pre- treatment) or during ICPI treatment. Imaging features may be used to identify toxicity earlier (before symptoms), enabling clinicians to better manage toxicities at an earlier stage and/or to personalise follow up in patients at higher risk of ICPI-toxicity, and to optimise ICPI treatment. This project will involve significant work in curating clinical and imaging data along with application and development of new techniques for pattern recognition (deep learning) working with clinical trial data from AstraZeneca and real-world data from Addenbrooke's hospital. At the end of the project, the candidate will be an expert in the mathematics of medical imaging, application of machine learning techniques and the prediction of adverse events in cancer treatment. Who we are looking for: The candidate must qualify for home (UK) tuition fees due to funding constraints. The candidate must hold (or be predicted to achieve) the equivalent of a first-class or upper-second undergraduate degree in biomedicine, Mathematics, computer science, or a general natural science. Experience with computer languages and code writing are highly relevant for the project and an aptitude for computer programming is essential. An interest in applying technology to healthcare settings is important. The candidate must be self-motivated, a good problem solver, a great communicator, and be able to work cohesively within a multi-disciplinary team. What we can provide: Along with a highly-competitive stipend and a fantastic environment to live and work in, the appointee will receive the necessary training in applied mathematics, machine learning and the latest understanding of the clinical manifestations and mechanisms causing toxicity to cancer treatments. Your supervisors will provide private tuition to develop further skills that you need to optimally contribute to the success of the PhD project. You will also be equipped with any necessary technology and support for travel to conferences, research visits and workshops as needed. This PhD also provides a unique opportunity to work across the academic and commercial sectors between two world class institutions (University of Cambridge and AstraZeneca). How to Apply? Please submit a short CV (maximum 2 pages), which should include two referees and a statement of research interest, by email to Michael Roberts at mr808@cam.ac.uk. The successful applicant will be required to complete a formal application to the University of Cambridge (www.graduate.study.cam.ac.uk/how-do-I-apply). Applications will be accepted from within the UK and from non-UK countries. Funding note: This project is funded by an AstraZeneca Non-Clinical studentship awarded to the University of Cambridge. Funding is for 3.5 years and will cover tuition fees (at the Home rate), stipend and allocation toward project costs and training. The successful candidate is expected to start early October 2022. Please quote reference LE29621 on your application and in any correspondence about this vacancy. The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. We particularly welcome applications from women and /or candidates from a BME background for this vacancy as they are currently under-represented at this level in our Department. The University has a responsibility to ensure that all employees are eligible to live and work in the UK. Department/Location Department of Applied Mathematics and Theoretical Physics Reference LE29621 Category Studentships Published 24 December 2021 Closing date 31 January 2022 Share Share this job on your social network. 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