Job description

Do you want to help us improve human health and understand life on Earth? Make your mark by shaping the future to enable or deliver life-changing science to solve some of humanity’s greatest challenges.

About The Role

We seek two Principal Research Data Scientists with machine learning knowledge to participate in a collaborative project between The Wellcome Sanger Institute and The Alan Turing Institute. This collaborative project will leverage investments in observing biological systems at cellular resolution (such as the Human Cell Atlas (HCA) data) to develop state-of-the-art generative machine learning (ML) tools that can model cellular behaviours across various modalities and scales. You will work within an interdisciplinary team of life and computer/ML scientists, with a shared goal of improving our understanding of the rules of life and using this to improve health for all. This role will sit within a new AI/ML Faculty group led by Dr Mohammad Lotfollahi in the Cellular Genetics Programme, and you will be responsible for delivering your portfolio of scientific research projects as part of the broader team strategy. These positions are for 3 year fixed term contracts.

About The Project

Your work will contribute to the overall aims of this collaboration which are to address two fundamental yet significant questions:

1- “Can we predict cellular responses to perturbations (e.g., drugs, genetic perturbations)?” and,

2-“Can we predict molecular information (e.g., gene expression) at a cellular resolution from within a tissue given the histology image?”.

Our teams are well-positioned to tackle this problem with experience in both generating and analysis datasets, including millions of cells across multiple tissues and conditions (e.g., disease, healthy), a detailed understanding in the training of large-scale ML models and a track record of undertaking large data-science projects.

You Will Be Responsible For

  • Independently manage and lead machine learning research projects and write outcomes in a scientific publication for submission to journals or machine learning conferences (ICLR, ICML, CVPR, etc).
  • Collaborate with team members, propose, develop, and evaluate new machine learning models that enable understanding single-cell data and its application in drug discovery.
  • Work with PhD students and postdocs in collaborating teams on developing solutions for interdisciplinary scientific problems in biology, providing supervision and training to junior members of the team.
  • Contribute to writing scientific papers on biotechnology and biology.
  • Distil your developed solutions into open-source and easy-to-install packages with documentation that facilitates the usage of your solution for downstream users, including biologists and bioinformaticians.
  • Present your research and analysis pipelines to internal and external audiences.

About You

You will be supported in your personal and professional development and have the opportunity to lead peer-reviewed publications around using genetics and genomics approaches to guide drug discovery and present them at national and international conferences.

Essential Skills

  • Ph.D. or MS.c with equivalent research experience in a relevant quantitative discipline (e.g., Computer Science, Computational Biology, Genetics, Bioinformatics, Physics, Engineering, or Applied Statistics/Mathematics)
  • Proven experience using advanced statistical techniques, machine learning, and modern deep learning techniques.
  • Previous ML work experience in scientific/academic environment (RA/Internships are considered as work experience)
  • Strong knowledge of Python, including core data science libraries such as Scikit-Learn, SciPy, TensorFlow, and PyTorch.
  • Knowledge of software development good practices and collaboration tools, including git-based version control, python package management, and code reviews.
  • Excellent communication skills, with the ability to explain complex machine learning algorithms and statistical methods to non-technical stakeholders.
  • Evidence of related work experience as a researcher in the area of Machine learning
  • Strong publication record, first author position ideal
  • Ability to quickly understand scientific, technical, and process challenges and breakdown complex problems into actionable steps
  • Ability to work in a frequently changing environment with the capability to interpret management information to amend plans
  • Ability to prioritize, manage workload, and deliver agreed activities consistently on time
  • Demonstrate good networking, influencing and relationship building skills
  • Strategic thinking is the ability to see the ‘bigger picture'
  • Ability to build collaborative working relationships with internal and external stakeholders at all levels
  • Demonstrates inclusivity and respect for all

Relevant Publication Of The Groups

  • Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M. & Others. Mapping single-cell data to reference atlases by transfer learning. Nature Biotechnology 1–10 (2021).
  • Lotfollahi, M., Wolf, F. A. & Theis, F. J. scGen predicts single-cell perturbation responses. Nature Methods 16, 715–721 (2019).
  • Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-Zadeh, S., Talavera-López, C., Misharin, A. V. & Theis, F. J. Biologically informed deep learning to query gene programs in single cell atlases. Nature Cell Biology (2023).

Other Information

Salary per annum: £52,152-£61,957

Hybrid Working: We support hybrid working, this can be discussed further at interview stage.

Application Process

Please upload your current CV and a cover letter outlining how you meet the criteria set out above.

As part of the collaborative nature of this project, representatives from the Turing Institute will be involved in the recruitment process and your details will be shared with them.

Closing date: 31st December 2023

Interviews will take place week commencing 15th January 2024 and 22nd January 2024.

Applications will be reviewed on an ongoing basis and the role may close ahead of the closing date if we receive a sufficient number of applications.

Hybrid Working At Wellcome Sanger

We recognise that there are many benefits to Hybrid Working; including an improved work-life balance, with more focused time, as well as the ability to organise working time so that collaborative opportunities and team discussions are facilitated on campus. The hybrid working arrangement will vary for different roles and teams. The nature of your role and the type of work you do will determine if a hybrid working arrangement is possible.

Equality, Diversity And Inclusion

We aim to attract, recruit, retain and develop talent from the widest possible talent pool, thereby gaining insight and access to different markets to generate a greater impact on the world. We have a supportive culture with the following staff networks, LGBTQ+, Parents and Carers, Disability and Race Equity to bring people together to share experiences, offer specific support and development opportunities and raise awareness. The networks are also a place for allies to provide support to others.

We want our people to be whoever they want to be because we believe people who bring their best selves to work, do their best work. That’s why we’re committed to creating a truly inclusive culture at Sanger Institute. We will consider all individuals without discrimination and are committed to creating an inclusive environment for all employees, where everyone can thrive.

Our Benefits

We are proud to deliver an awarding campus-wide employee wellbeing strategy and programme. The importance of good health and adopting a healthier lifestyle and the commitment to reduce work-related stress is strongly acknowledged and recognised at Sanger Institute.

Sanger Institute became a signatory of the International Technician Commitment initiative In March 2018. The Technician Commitment aims to empower and ensure visibility, recognition, career development and sustainability for technicians working in higher education and research, across all disciplines.

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