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At King’s College London, we have three MRC-funded PhD studentships in data science and AI. In particularly, please check out the machine learning project form Paul O’Reilly and me, which will build predictive models across genetics, environment and clinical risk factors (Project 2.6 below). The student will join the Statistical Genetics Unit at King’s College London, a thriving research group of 20 academics postdocs and PhD students working in related areas. https://kcl-mrcdtp.com/datascience-and-artificialintelligence This project will be suitable for a student with an academic background in mathematics, computer science or similar field. Applicants from the biological sciences will also be considered, but should be able to show their experience of quantitative skills, and commitment to undertaking a computationally intensive PhD. ----------------------------------------------------------- MRC DTP PHD STUDENTSHIPS IN DATA SCIENCE AND ARTIFICIAL INTELLIGENCE Applications open: Monday 30th April Applications close: Monday 28th May, 23:59 King’s College London are seeking 3 outstanding and motivated students to join our health faculties. Human health is a dominant and unifying research theme at King’s College London. We are internationally recognised for excellence in our training programmes in biomedical and health research. MRC DTP PhD Studentships in Data Science & Artificial Intelligence Three 3.5 year fully-funded PhDs are available to commence in October 2018. The successful candidates will pursue a 3.5 year PhD and will be part of the MRC Doctoral Training Partnership. They will start immediately on their PhD research project in October 2018 and will further benefit from the core and specialised research training, personal mentorship and cohort activities that are provided through the MRC DTP. Projects All projects are available as a straight 3.5 PhD. Applicants must apply to one project only. You are advised to contact project supervisors for further information about the projects. 1.6 Deep Learning to Predict Outcome in Cancer Patients 2.6 Machine Learning of Genetic, Clinical and Environmental Data for Early Morbidity Detection in the UK Biobank 3.6 Network Science Strategies for Antibody-Based Cancer Therapy You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.