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

Rosalind Franklin Institute; Harwell; Vor Ort; GBP 36.000 - 43.000; Vollzeit

Benefits dieser Stelle

Hybrid and flexible working

Health and wellbeing support

Generous pension (up to 18%)

Free on-site parking

Perkbox access

Cycle to Work Scheme

Canteen on campus

Zusammenfassung

The Rosalind Franklin Institute invites applications for a Research Associate to advance molecular mapping of cells using machine-learning methods on cryo-electron tomograms. You will work with both experimental and computational researchers to develop and validate new detection and classification approaches for proteins and macromolecular complexes.

The role requires a PhD or near-completion and offers opportunities for scientific independence within a collaborative, interdisciplinary team on

Qualifikationen

• PhD/DPhil (or equivalent) is required or expected at appointment.
• Experience with ML methods for imaging and cell analysis is preferred.
• Ability to work across experimental and computational teams.

Aufgaben

• Develop ML methods to detect and classify proteins in cryo-EM tomograms.
• Integrate structural, molecular and imaging data to guide analysis.
• Prepare datasets, evaluate methods and validate biological findings.
• Develop reproducible workflows and research software with documentation.
• Lead major contributions to outputs such as papers and presentations.
• Supervise students or visitors and foster scientific independence.

Kenntnisse

Machine learning

Python

Data analysis

Collaboration

Ausbildung

PhD/DPhil (or equivalent)

Tools

PyTorch/TensorFlow

Subtomogram analysis

Data visualization

Jobbeschreibung

The Rosalind Franklin Institute (the Franklin) is a technology institute established by the UK Government as a unique centre committed to advancing tools that are needed to transform healthcare in the future.

The Institute brings together researchers in life and physical sciences, and engineering, to develop a spectrum of tools which we will use to image, interpret and intervene in biological systems. These insights will speed up the discovery of new medicines, help find new diagnostics and contribute to a deeper understanding of human health and disease. Our Science Strategy seeks to focus the Franklin's research and unite our researchers around our Technology Innovation Challenges and Life Science Challenges. For more information on the Franklin's Challenges click here.

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

As a Research Associate you will:

• 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.

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.

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 Thursday 26th November 2026.

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

Applicants should refer to our Candidate Privacy Policy

Area: Scientific

Location: Harwell Campus, Oxfordshire

Type of Post: Full Time (37.5 hours per week)

Period of appointment: 2 Years Fixed-Term Contract (with the possibility of extension)

Salary: From £39,500 per annum (depending on skills and experience)