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

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

31st July 2026

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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