Salary:

Research Assistant, Grade 5, £26,715 - £30,942
Research Associate, Grade 7, £32,816 - £40,322

The MRC Cancer Unit (MRC CU) is a University Department situated on the Cambridge Biomedical Campus. It provides an outstanding environment for cancer research, supporting some 10 research groups and ~150 bench scientists. The MRC CU's focus is to conduct world-leading research to understand how cancers develop and seeks to translate this knowledge into new approaches for diagnosis and treatment. The unit is a key participant in the Cambridge Cancer Centre, providing an opportunity to engage in interdisciplinary collaborations with Cambridge colleagues in the biological, physical, chemical and engineering sciences.

We seek a talented and highly motivated data analyst to join our exciting project applying artificial intelligence methods to understand immune and inflammatory (including immunotherapy) responses in cancer. The project will be based at the Systems Biomedicine lab https://www.samarajiwa-lab.org, MRC Cancer Unit, University of Cambridge. It involves applying deep learning and machine learning to multi-omic datasets in order to understand and predict immune responses in cancer. The project will utilise datasets generated by our experimental collaborators as well as publicly available biomedical big data. The lab has access to high-performance computing clusters and its own GPU server as well as a number of high-throughput genomic data processing pipelines.

The ideal candidate would have completed a BSc/MSc (or a Ph.D.) in machine learning, deep learning or in other quantitative fields such as computational biology, mathematics or engineering. Previous experience in immunology or cancer related fields would be advantageous. While knowledge of biology is not required, an enthusiasm for solving biomedical problems and acquiring the necessary genomics skill sets is expected. The successful candidate will work within an interdisciplinary team that includes clinicians, cancer researchers, computational biologists, and data scientists.

• Knowledge of machine learning or deep learning is required.
• High level of proficiency in Python and R programming is required.
• R programming skills advantageous. If the candidate does not have intermediate R programming skills, a willingness to learn and use R for biomedical data analysis is required.
• Good data science skills (data cleaning, processing, modelling and visualisation) required.
• Genomics or Next-generation sequencing related data analysis skills will be advantageous. Training will be provided to candidates that lack these skills.
• A willingness to practice reproducible research is required.

Appointment at Research Associate is dependent on having a PhD.  Where a PhD has yet to be awarded or submitted appointment will initially be made as a Research Assistant (£30,046pa) and amended to Research Associate (£32,816-£40,322pa) when the PhD is awarded.

If an individual has not submitted a PhD or is not working towards one but has a degree and/or Masters in a relevant area they could be appointed as a Research Assistant (£26,715-£30,942pa (Grade 5) dependent on relevant experience.

Fixed-term: The funds for this post are available for 9 months in the first instance.

Once an offer of employment has been accepted, the successful candidate will be required to undergo a security check.

To apply online for this vacancy and to view further information about the role, please visit :

http://www.jobs.cam.ac.uk/job/23297.

The closing date for applications is Sunday 20th October 2019 with interviews taking place week commencing 28th October 2019.

Please ensure that you upload your Curriculum Vitae (CV) and a covering letter in the Upload section of the online application. The covering letter should outline how you match the criteria for the post and why you are applying for this role. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.

Please include details of your referees, including e-mail address and phone number, one of which must be your most recent line manager

Please quote reference SK20761 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.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.