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(Postdoctoral) Research Assistant in Machine Learning

Women's & Reproductive Health

University of Oxford, Oxford, UK


  • Posted on: 24th Jan 2022
  • Salary: Grade 6/7: £29,614 to £40,927 per annum
  • REF: 155784
  • Closes: 31st January 2022
  • Contract Type: Fixed-Term/Contract
  • Hours: Full Time

Job Details

(Postdoctoral) Research Assistant in Machine Learning

Nuffield Department of Women’s & Reproductive Health, Level 3 Women’s Centre, John Radcliffe Hospital, Oxford

Grade 6/7: £29,614 to £40,927 per annum


We invite applications for the position of Research Assistant or Postdoctoral Research Assistant in Machine Learning to join theDeep Medicineprogramme at the Nuffield Department of Women’s and Reproductive Health (NDWRH), University of Oxford. The successful candidate will join a multi-disciplinary group of machine learning scientists, epidemiologists and clinicians at Deep Medicine who lead pioneering research in data-driven health care and precision medicine.

You will be expected to build upon and advance Deep Medicine’s pioneering work on the applications of novel learning paradigms and modelling approaches in machine learning to large-scale, longitudinal UK Electronic Health Records (EHR). The project aims to leverage such methods to identify clusters of patients that show distinct trajectories and might, therefore, respond differently to treatments. Working with some of the largest and most comprehensive EHR in the world, this is a unique opportunity to transfer and apply tools and techniques from machine learning and conduct high-impact research, while contributing to the broader goals of Deep Medicine.

This a prestigious position funded by Novo Nordisk and is part of an ambitious consortium of academic and industrial collaborators with world-leading expertise in machine learning and in-silico trials. The project aims to push the boundaries of precision medicine in heart failure. This role will provide a unique opportunity to enjoy research in machine learning and health care; grow and be challenged in a multi-disciplinary environment; and create game-changing solutions for health care. It further offers a great opportunity to develop a high-profile academic career, through taking a leadership role in the field of healthcare/biomedical informatics.

You will hold a minimum of BSc or an equivalent qualification(PhD/DPhil at grade 7, or near completion) in computer science, statistics, mathematics, engineering or other relevant areas. You will have a strong foundation in advanced AI topics (e.g., Bayesian and probabilistic machine learning, deep learning, sequence models, NLP), up-to-date knowledge about novel paradigms and methods in ML, especially DL, and advanced programming skills in Python (and their related data processing, machine learning, deep learning, and visualisation libraries).

You will be proficient in working with ML modules such as scikit-learn, TensorFlow, PyTorch, Keras Tuner and big-data frameworks such as Dask and PySpark andalso have practical experience in preparing data for Machine Learning (e.g., using SQL and/or NoSQL technologies). You will have experience, familiarity, or willingness to learn statistical and epidemiological analysis methods. Excellent communications skills and the ability to work independently are also essential for this role.

For an informal discussion about the post, please contact Prof Kazem Rahimi (kazem.rahimi@wrh.ox.ac.uk).

This position is full-time and fixed-term for 30 months. This post is available from 1st April 2022. Applications for flexible working arrangements are welcomed and will be considered in line with business needs.

You will be required to upload a CV and Supporting Statement as part of your online application. Click here for information and advice on writing an effective Supporting Statement: https://www.jobs.ox.ac.uk/cv-and-supporting-statement.

The closing date for applications is 12.00 noon on Monday 31st January 2022.

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Recruitment Administrator

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Closing Date & Time :

31-Jan-2022 12:00

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155784_RA-PDRA in Machine Learning_Job Description.pdf


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