Research Associate in Machine Learning for Cryo Electron Tomography (10417)

Rosalind Franklin Institute; East Hagbourne; Hybrid; GBP 35,000 - 42,000; Full time

Benefits offered by this job

25 days holidays

Generous pension scheme

Group Life Assurance

Buying and Selling Annual Leave

Hybrid and flexible working

Training and development opportunities

Bus pass discount scheme

Cycle to Work Scheme

Perkbox employee platform access

Occupational Health support

Job summary

The Rosalind Franklin Institute invites applications for a Research Associate to advance molecular mapping in cryo-electron tomograms. The role will develop machine-learning methods for particle localisation, detection and classification in crowded cellular volumes, with emphasis on learning from limited annotations and generalisation to new datasets and molecular species.

Working with experimental and computational colleagues, you will build robust benchmarks, create reproducible software, and

Qualifications

• PhD/DPhil (or equivalent) required; candidates with submitted thesis considered.
• Experience applying machine learning to image data and subtomogram analysis.
• Strong publication record and ability to work in interdisciplinary teams.

Responsibilities

• Develop and apply machine-learning methods to broaden detection in cryo-electron tomograms.
• Collaborate with experimental and computational colleagues to prepare datasets and validate findings.
• Develop reproducible analysis workflows and software, and share results via documentation and publications.
• Plan data acquisitions with experimentalists and deliver outputs such as reports or presentations.
• Contribute to outputs from research including papers and presentations; supervise students or visitors.

Skills

Machine learning

Data analysis

Scientific writing

Team collaboration

Education

PhD or equivalent

Tools

Python

Machine Learning Frameworks

Job description

This research opportunity within the Virtual Cell project will advance molecular mapping of cells by expanding the range of proteins and macromolecular complexes that can be reliably detected and characterised in cryo-electron tomograms and develop methods to track how these change over time and in response to cellular perturbation.

The Research Associate will develop machine-learning methods for particle localisation, detection and classification in crowded cellular volumes. The work will emphasise learning from limited annotations, generalisation to additional molecular species and experimental datasets, and discovery of candidate particle populations beyond existing labelled classes. Methods will address low signal-to-noise ratios, missing-wedge artefacts, and variation in particle orientation, abundance and conformation.

The role will explore integration of complementary structural, molecular and imaging data, such as predicted or experimental protein structures, proteomics, molecular interaction data, and fluorescence or correlative microscopy. These sources will guide detection, constrain candidate molecular identities and support validation, with explicit consideration of their uncertainty, spatial resolution and potential biases.

Working closely with experimental and computational researchers, the role-holder will establish robust benchmarks, develop reproducible software, and validate detections and candidate identities through subtomogram analysis and complementary evidence. The role offers opportunities to develop scientific independence within an interdisciplinary team.

Key Responsibilities

• * Develop and apply machine-learning methods to broaden the range of proteins and macromolecular complexes that can be detected and characterised in cryo-electron tomograms.
• * Explore how complementary structural, molecular and imaging data can improve analysis and support interpretation of molecular organisation in cells.
• * Work with experimental and computational colleagues to prepare datasets, evaluate methods and validate biological findings.
• * Develop reproducible analysis workflows and research software, and share methods and findings through documentation, publications and presentations.
• * Plan data acquisitions with experimentalists who conduct them, analyse and interpret results and supervised 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 Franklin's 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 of 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 can be found at the bottom of the page.

This job description sets out the skills and experience 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).

In return we offer:

• * 25 days holidays plus bank holidays and Christmas holiday shutdown
• * Generous pension scheme (employer’s contribution currently up to 18%)
• * Group Life Assurance (also known as Group Life Insurance or Death in Service)
• * Buying and Selling Annual Leave
• * Workplace Nursery Salary Sacrifice Scheme
• * Hub building with state-of-the-art laboratories
• * Hybrid and flexible working
• * Training and development opportunities for staff at all levels
• * Bus pass discount scheme and good transport links to Oxford and surrounding areas
• * Cycle to Work Scheme
• * Access to employee discount platform (Perkbox)
• * Occupational Health and wellbeing support including Employee Assistance (24/7 support and counselling)
• * Health Cash Plan
• * Subsidised canteen and food outlets on campus
• * Free on-site parking
• * Campus location in beautiful countryside with social and sports clubs open to staff

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 [e-mail] to discuss your requirements confidentially.

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

Closing date: The closing date for applications is 23:59 on Thursday26th November 2026.

Interviews are planned for w/c 7th December 2026.

Applicants should refer to our Candidate Privacy Policy