Cancer Computational Research Associate

London (Greater) (GB)
£38,304 per annum, including London Weighting Allowance
09 Jun 2021
13 Jun 2021
024725
Life sciences, Biological Sciences
Fixed Term
Full Time

Job description
 
The Cancer Bioinformatics group (  http://cancerbioinformatics.co.uk/) within the Breast Cancer NOW Unit, School of Cancer and Pharmaceutical Sciences, at King’s College London, is currently seeking a Research Associate with expertise in machine learning and computer science in the field of translational cancer research to join our efforts in understanding the histological and molecular alterations of triple negative breast cancer. This position will function under the supervision of Dr. Anita Grigoriadis. 
This position offers a stimulating and multidisciplinary environment. Women carrying germline BRCA1/2 have a 65-70% lifetime risk of developing breast cancer. Current management options are either risk reducing mastectomy or intensive screening, both of which can have a significant impact on those women who remain cancer free. Germline BRCA1/2 carriers would, therefore, benefit from additional tools to predict their risk of developing cancer. The post-holder wil  implementing novel artificial intelligence(AI)-based computational approaches for digital pathology which are indicative of early signs of breast cancer. 
 
Applicants should have a strong computational background and expertise in machine learning methods for image analyses. Experience with digitised whiole slide images is desirable. Applicants will hold a relevant postgraduate degree, or equivalent experience in a relevant role. The ideal candidate should be passionate about the translation research into clinical practice and be highly motivated to work in a fast-paced and highly collaborative environment. 
The post will be based in Dr Grigoriadis Cancer Bioinformatics Team at the Cancer Centre at Guy’s Hospital Campus, and will also involve interaction with colleagues from the Cancer Center at the University College London and UK-based and US-based software companies working in the field of digital pathology. 
 
 
This post will be offered on an a fixed-term contract for 12 months 
This is a full-time post - 100% full time equivalent

Key responsibilities

  • To be able to develop novel deep learning methods for image analyses.
  • To be able to use standard statistical methods.
  • To be responsible for establishing workflows to process digitised whole slide images, integrating internal with external datasets and interpreting these results.
  • To prepare data and workflows to analyse that data in such a way that it can be shared with other collaborators.
  • To oversee the maintenance of information and documentation regarding the implemented scientific software solutions and developed methods.
  • To provide independent advice and to deal with scientific and technical queries regarding data analysis and computational biology.
  • To be able to explain high-dimensional data analysis to computational biologists, bioinformaticians and non-computational biologists.
  • To prepare his/her work in the form of a manuscript for publications and presentation at local and international research meetings
  • To present results of own research at group meetings and in written/figure form where necessary
  • To contribute to discussions with research colleagues to identify appropriate experiments.
  • To contribute to the production of research reports and publications
  • To form effective networks with colleagues in other groups of the department and with peers in King’s College London at large to ensure smooth and effective running of the group’s facilities and to identify opportunities for the group to benefit from facilities elsewhere
  • To work with other members of the team to generate data.

 
The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post.


Skills, knowledge, and experience


Essential criteria

 

  1. A relevant postgraduate degree, or equivalent experience in a relevant role.
  2. MSc in computer science, machine learning, bioinformatics or any other bioscience with a strong numerate component such as mathematics and statistics.
  3. BSc in computer science, bioinformatics, statistics or a similar academic field
  4. Previous experience with AI-based approaches
  5. Understanding of histopathology
  6. Fluent in at least one statistical programming language (e.g. R or Matlab)
  7. Fluent in as least one  programming language (e.g. Shell, Perl, Python, or C++)
  8. Experience with HPC environments
  9. Excellent interpersonal and communication skills, and the ability to liaise with other members of the group
  10. Ability to present complex data at meetings (internal and external audiences, including international conferences)
  11. Ability to analyse data, meet deadlines, and write up results in a timely manner
  12. Written and published high quality novel research in peer-reviewed journals and able to demonstrate manuscript writing skill ability

 
Desirable criteria

  1. Experience of collaborating with clinicians and wetlab scientists
  2. Motivated to achieve further training in advanced machine learning
  3. Ability to prioritise workloads, meet deadlines and organise work accordingly

*Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6.
 
Further information
We have build large datasets of whole slide images from breast and head & neck cancers and patient-matched cancer-free and cancer-involved lymph nodes. Working with AI-based digital diagnostics companies, we aim to develop novel tools which in the future will support pathologists’ assessment.