Postdoctoral Fellowship in Image-Based Carcinogen Detection en Embl

Your supervisors & their groups:

Jess Ewald:

Jess Ewald, EMBL-EBI’s newest Research Group Leader is using machine learning and cell profiling to characterise chemical hazards to humans and ecosystems.

The group, led by Jess Ewald, uses machine learning and multi-omics integration to identify and characterise chemical hazards to humans and ecosystems. The team analyses omics data generated from cellular perturbations, with a particular focus on image-based cell profiling.

Find out more about Jess’ group, here: Ewald Research Group

Tim Coorens:

Tim Coorens joined EMBL-EBI as a Research Group Leader in May 2025 after completing his postdoctoral research with Prof. Gad Getz and Dr. Kristin Ardlie at the Broad Institute of MIT and Harvard, where he was an EMBO long-term fellow.

Find out more about Tim’s group, here: Coorens Research Group

Your role:

The Ewald Lab and Coorens Lab at EMBL-EBI are seeking a postdoctoral researcher to develop deep learning methods for predicting the carcinogenic effects of environmental chemical exposures using H&E histopathology images and genome sequencing data. This project is in collaboration with Arun Pandiri and other researchers at the US National Institute of Environmental Health Sciences.

Project context:

The U.S. National Toxicology Program (NTP) has evaluated more than 3,000 substances, including environmental and food contaminants, botanicals, pharmaceuticals, radiation, and mixtures, using a wide range of in vitro and in vivo models. These efforts have produced an unparalleled archive of H&E-stained rodent tissues, as well as extensive genomic datasets, that together represent a unique and underutilized resource for studying mechanisms of chemical carcinogenesis.

Cancer development is tightly linked to the accumulation of somatic mutations, yet carcinogens act through distinct pathways: some cause tumorigenesis by directly inducing mutations, while others selectively expand pre-existing clones with oncogenic mutations. Importantly, applying image-based deep learning to the NTP tissue archive offers the ability to detect cellular and tissue morphological alterations that precede neoplasia and to discover patterns that may help predict apical outcomes.

Integrating these image-based insights with genomic alterations provides a powerful framework to reveal early biomarkers of carcinogenicity, improve mechanistic understanding of chemical hazards, and ultimately enable efficient short-term screening strategies to predict carcinogenicity and reduce our reliance on 2-year rodent bioassays. 

Beyond the overall project objective, possible research directions include:

• Leveraging single-cell and spatial transcriptomics databases to improve histology-based predictions
• Developing interpretable deep learning models to link cell- and tissue-level changes
• Integrating image-derived features with molecular and organism-level outcome data, including mutational signatures

For examples of research from both labs, see this recent preprint on machine learning for cell morphology analysis, and recent publications [1, 2] on the landscape of somatic mutations in normal tissues.

You have:

• A PhD or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated computational experience
• Strong scientific programming skills, preferably in Python or R
• Expertise in statistical methods, mathematical modelling, and/or machine learning
• Demonstrated ability to lead research publications in international peer reviewed journals (pre-prints accepted)
• Excellent communication, collaboration, and leadership skills
• The desire to be a supportive, creative, and responsible team member.

You may also have:

• Expertise in computational toxicology or pharmacology, from either a pharmaceutical or regulatory perspective
• Experience with computational image analysis
• Background in cancer genomics or related computational cancer field
• Experience with ML/AI frameworks like PyTorch, Keras, Pyro or TensorFlow.

Contract length:

2-year grant limited fixed term contract.

Salary:

Year 1 Stipend at rate of £3,383.52 per month after tax but excluding pension and insurance contributions.

Next Steps:

This vacancy will run from Monday, 20th October with a scheduled closing date of Sunday, 16th November - We invite you to submit your application as soon as possible. Please include your up-to-date CV, cover letter and two letters of recommendation for consideration.

Should you be selected to be invited to interview, we will ask for two letters of recommendation to be provided ahead of your scheduled interview date.

Professional development support:

The EMBL Fellows’ Career Service provides support and guidance to predoctoral and postdoctoral fellows across all six EMBL’s sites.

Working with a dedicated Careers Advisor, this invaluable service will help you to take informed decisions about your career planning both in the short and longer term. Whether your main interest is pursuing a career path in academia, exploring opportunities in industry or exploring an independent venture, the EMBL Fellows’ Career Service will provide you with a portfolio of activities and resources to help you.

To find out more please visit - EMBL-fellows-career-service.

Professional development support

The EMBL Fellows’ Career Service provides support and guidance to predoctoral and postdoctoral fellows across all six EMBL’s sites.

Working with a dedicated Careers Advisor, this invaluable service will help you to take informed decisions about your career planning both in the short and longer term. Whether your main interest is pursuing a career path in academia, exploring opportunities in industry or exploring an independent venture, the EMBL Fellows’ Career Service with provide you with a portfolio of activities and resources to help you.

To find out more please visit - EMBL-fellows-career-service

Why join us

Join a culture of innovation 
We are located on the Wellcome Genome Campus, alongside other prominent research and biotech organisations, and surrounded by beautiful Cambridgeshire countryside. This is a highly collaborative and inclusive community where our employees enjoy a relaxed atmosphere. We are committed to ensuring our employees feel valued, supported and empowered to reach their professional potential. 

Enjoy lots of employee benefits:

• Financial incentives: Monthly family and child allowances, generous stipend reviewed yearly, pension scheme, death benefit and unemployment insurances

• Flexible working arrangements including hybrid working patterns 

• Private medical insurance for you and your immediate family 

• Generous time off: 30 days annual leave per year in addition to public holidays

• Campus life: Free shuttle bus to and from work, on-site library, subsidised on-site gym and cafeteria, casual dress code, extensive sports and social club activities (on campus and remotely)

• Family benefits: On-site nursery (Heidelberg & Hinxton), 10 days of child sick leave, paid maternity & parental leave, holiday clubs on campus and monthly family and child allowances

• Benefits for non-residents: Visa and financial support to relocate if you're overseas

What else you need to know

• International applicants: We recruit internationally and successful candidates are offered visa exemptions. Read more on our page for international applicants. 

• Diversity and inclusion: At EMBL, we strongly believe that inclusive and diverse teams benefit from higher levels of innovation and creative thought. We encourage applications from women, LGBTQ+ & individuals from all nationalities. 

• Job location: All our fellowships are based on-site (for at least part of each week). If you are living overseas, you will receive a generous relocation package to support you. 

• EMBL is a signatory of DORA. Find out how we apply DORA principles to our recruitment and performance assessment processes here.

• Watch this video to find out what it's like to be a Postdoc at EMBL-EBI.

• How to apply: To apply please submit a cover letter and a CV through our online system. Applications will close at 23:59 CET on the date shown below. We aim to provide a response within two weeks after the closing date below.

Closing Date