Research Associate in Machine-Learning Modelling of 2-D and 3-D Images (10406)

Rosalind Franklin Institute

This is an exciting research opportunity to join the VirtuAI Cell project which aims to develop virtual mapping of intracellular contents of biological cells. This leads to the ultimate goal of running in-silico cell simulations that use such mappings to model how the dynamics of intracellular contents may modulate in response to various types of cell disturbances; e.g., due to the injection of a drug molecule; or the intracellular growth of cancer promoting mutations, the checking of which one could model to predict new routes towards preventative cancer.

The Research Associate will focus on developing machine-learning (ML) approaches that automatically segment and classify cellular species from experimental 2-D microscopy and 3-D tomography data, to help build a virtual mapping of biological cells. The role-holder will also explore new ways to represent data to more efficiently identify cellular species and enable better downstream ML modelling tasks. The work may also involve designing, building and deploying new feature selection and optimisation strategies for the ML modelling of cellular data. More generally, this role offers a great opportunity for an ML expert to become a valued member of a highly interdisciplinary, challenge-focused project team that aims to solve a global research challenge.

As a Research Associate at the Franklin, you will bring scientific knowledge and skills to deliver a specific research project and/or you will bring independent, creative science, or specific skills to a team delivering a project or program. Through this work, you will build scientific independence, develop new science and leadership skills, and establish a growing reputation externally.

Key Responsibilities

As a Research Associate, you will:

• Analyse and interpret experimental datasets of 2-D microscopy images of cells via ML methods
• Analyse and interpret experimental datasets of 3-D tomograms of cellular data via ML methods
• Create data representations that help to develop efficient ML modelling of cellular data
• Perform feature selection and optimisation of sub-cellular data using ML modelling
• Construct, train, evaluate and deploy ML models for application to cell biology
• Curate large volumes of simulated data to assist with ML modelling for cell biology
• Undertake data cleaning, normalisation and splitting tasks as needed
• Perform or supervise data annotation where necessary
• Improve ML models via hyperparameter optimisation and/or fine-tuning parameters
• Integrate ML models into larger software systems, including live system architectures
• Develop automation tools that streamline ML-based predictions for cell biology
• Develop multi-fidelity approaches to ML modelling where needed
• Develop new ML algorithms for cell-biology applications where needed
• Plan data acquisitions with experimentalists who conduct them, analyse and interpret results and supervise delivery of outputs (e.g. research report, patent application) in a scientific/technology area of interest.
• Work within a project team, contributing to wider projects around key Challenges.
• Lead major contributions to outputs from research including papers, patents and both internal and external presentations.
• Support and develop others including day-to-day supervision of students or visitors in areas related to own research.
• Have supervised, staged progression to first stages of scientific independence with opportunities to further develop science and skills/experience.
• Enhance your research through collaboration with other researchers and make active contributions to exchanging ideas through your own network.
• Be able to understand, interpret, create and communicate appropriately within a research context.
• Develop search and discovery skills and techniques.
• Be supervised by a Scientist/Senior Scientist in delivery of research outputs, either in the context of a project or Challenge or as an early career development fellow.

Before submitting your application, please ensure you read the Job Information Pack for full details of this role.

This job description sets out the skills and experiences we believe are needed to be able to do this job but, research also tells us women are much more likely than men to take this list of requirements as absolute and self-select out of the process. If you think you can deliver this role then we want to hear from you, regardless of the boxes you did not tick.

Whilst the role requires candidates to hold a PhD/DPhil (or equivalent), we may consider candidates who have submitted their PhD/DPhil thesis, in which case the initial appointment will be made at £38,500 per annum (to be increased on completion of the PhD/DPhil qualification).

The Franklin’s underlying aim is to produce the best science for research today, and this means resolutely embracing a diverse team, who have a wide range of experiences, skills and knowledge to push forward on the innovative work our institution delivers. Both our work and our institution are better for it. For further information, view our Equality, Diversity and Inclusion Policy. 

We are committed to creating an inclusive environment where every applicant has an equal opportunity to showcase their talents and abilities. This includes making adjustments for candidates with specific needs. Please contact us at recruitment@rfi.ac.uk to discuss your requirements confidentially.

At the Rosalind Franklin Institute we also welcome applications from all around the world!

How to Apply

To be considered for this role, please upload a CV and cover letter explaining why you think you are the right person for this job. The link to apply is provided via the ‘Apply’ button above.