Topological Data Analysis Research Associate — Cell Biology

BioUK; United Kingdom; Hybrid; GBP 35,000 - 42,000; Full time

Benefits offered by this job

25 days holidays + bank holidays

Generous pension scheme

Group Life Assurance

Workplace Nursery Salary Sacrifice

Hybrid and flexible working

Training and development

Cycle to Work Scheme

Perkbox access

On-site parking

Job summary

Rosalind Franklin Institute is seeking a Research Associate in Topological Data Analysis for Cell Biology to develop and apply persistent homology on experimental microscopy and tomography data. The role offers opportunities to advance multi-scale analyses and contribute to ML modelling of cellular data within a cross-disciplinary team.

The successful candidate will join a project focused on creating virtual mappings of intracellular contents and will have access to state-of-the-art facilities

Qualifications

• PhD/DPhil is required, or candidates with a submitted PhD/DPhil thesis may be considered at an initial appointment.
• Experience applying persistent homology/Topological Data Analysis to biological data and imaging is essential.
• Strong analytical and communication skills, and ability to work in an interdisciplinary team.

Responsibilities

• Employ persistent homology to represent and classify structures in cell biology from 2-D/3-D imaging data.
• Analyze results in collaboration with cell biology experts.
• Apply TDA to track multi-scale structural representations over time and resolutions.
• Contribute to ML feature engineering and model improvement for cellular data.
• Lead outputs such as papers, talks, and patent applications in a research context.

Skills

Persistent homology

Topological data analysis

Machine learning

Data analysis

Education

PhD in a relevant field

Tools

Python

MATLAB

Job description

Rosalind Franklin Institute is seeking a Research Associate in Topological Data Analysis for Cell Biology to develop and apply persistent homology on experimental microscopy and tomography data. The role offers opportunities to advance multi-scale analyses and contribute to ML modelling of cellular data within a cross-disciplinary team.

The successful candidate will join a project focused on creating virtual mappings of intracellular contents and will have access to state-of-the-art facilities