Job description

Seeking a highly motivated Research Associate with expertise in deep learning and spatial biology within the field of translational cancer research. The project will entail algorithm development and deployment of deep learning approaches for histomorphological characterisation of triple negative breast cancers. Through spatial transcriptomics, diverse transcriptional profiles will be mapped to regions of interest to decipher immune cell population dynamics in triple negative breast cancers. Subclonal and clonal T cell dynamics in these samples can be further investigated using TCR-sequencing.

This applicant will join the multidisciplinary Cancer Bioinformatics group (https://cancerbioinformatics.co.uk/) within the Breast Cancer Now Research Unit at King s College London whose collective aims are to implement computational approaches for the integrated multi-modal analyses from model systems and patient studies of triple-negative breast cancers. We lead on the latest technologies to perform subcellular spatial transcriptomics and hold a wealth of whole slide image data for digital pathology approaches.

Applicants should have a strong computational background and expertise in dealing with large datasets. Excellent programming, data analysis, statistics and deep learning skills are required. Experience with diverse molecular data is desirable. Applicants will hold a postgraduate degree or equivalent experience in a relevant role. The ideal candidate should be passionate about translating analytical approaches into clinical practice and highly motivated to work in a fast-paced and highly collaborative environment.

The post will be based in Dr Grigoriadis Cancer Bioinformatics Team at Guy s Cancer Centre at Guy s Hospital and will be expected to interface with colleagues, clinical investigators, and other research fellows during design, development and testing of algorithm.

This is a full time post

This is a Fixed-term post for 24 months

Key responsibilities

• To be able to develop, implement, and adapt novel deep learning approaches.
• To be able to use standard statistical and bioinformatics approaches.
• To be responsible for establishing workflows to process and integrate multi-modal omics data, interpret results and corroborate findings in external datasets.
• To prepare data and workflows in such a way that it can be shared with other collaborators.
• To oversee the maintenance of information and documentation regarding the implemented approaches and developed methods.
• To provide independent advice and 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 during group meetings and in written/figure form where necessary.
• To contribute to discussions with research colleagues to identify and design 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 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.

Skills, knowledge, and experience

Essential criteria

1. A relevant postgraduate degree or equivalent experience in a relevant role.

2. MSc in computer science, bioinformatics or any other bioscience with a strong numerate component such as mathematics or statistics.

3. Previous experience with deep learning methodologies, particularly in computer vision

4. Understanding of genomics and transcriptomics.

5. Fluent in at least one statistical programming language (e.g. R or Matlab).

6. Fluent in as least one programming language (e.g. Shell, Perl, Python, or C++).

7. Experience with computer vision, machine learning and deep learning libraries (e.g. OpenCV, Scikit-learn, Pytorch, Tensorflow)

8. Experience with HPC environments.

9. Excellent interpersonal and communication skills and the ability to liaise with other group members.

10. Highly self-motivated and hard working.

11. Ability to present complex data at meetings (internal and external audiences, including international conferences).

12. Ability to analyse data, meet deadlines, and write up results in a timely manner.

Desirable criteria

1. Experience in collaborating with clinicians and wet-lab scientists.

2. Motivated to achieve further training in advanced deep learning methodologies.

3. Ability to prioritise workloads, meet deadlines and organise work accordingly.

4. Writing and publishing in peer reviewed journals