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Quadram Institute Bioscience in Norwich invites applications for a Postdoctoral Research Scientist (Data Science) to join the Hildebrand group. You will harmonise large-scale dietary, metagenomic and metabolomic data across European cohorts and develop reproducible computational workflows.
The ideal candidate holds a PhD in bioinformatics or microbiology, with programming in Python or R and Bash. Experience with machine learning, mediation analysis and multi-omics data is desirable.
Postdoctoral Research Scientist (Data Science)
Job Title Postdoctoral Research Scientist (Data Science) Post Number 1006222 Closing Date 17 Nov 2026 Grade SC6 Starting Salary Salary: £39,000-£46,500
Funding End Date 30 Apr 2028 Hours per week 37 Project Title Gut microbial colonization and adaptation to the human host. Months Duration 20
Applications are invited for a Research Scientist to join the Laboratory of Prof Hildebrand in the Food, Microbes and Health programme at Quadram Institute Bioscience (QIB), Norwich, UK.
In this project we will investigate relationships between diet, the human gut microbiome and mental health across more than 20 European cohorts within the NUTRIMIND consortium. The successful applicant will harmonise large-scale dietary, metagenomic, metabolomic and mental-health datasets, establish reproducible statistical workflows, apply mediation approaches to investigate potential microbiome-mediated relationships, and develop machine-learning models to identify generalisable patterns using cross-validation. Building on earlier work from the hosting group (doi:10.1016/j.chom.2023.05.024; doi:10.1038/s41531-025-00885-5; doi:10.1016/j.chom.2021.05.008), the project will identify microbial communities, strains and genes associated with diet-microbiome-mentalbhealth relationships.
The successful applicant will work within a diverse, international consortium (NUTRIMIND, https://quadram.ac.uk/quadram-institute-joins-major-new-project-to-understand-gut-microbiome-diet-and-mental-health-connections/), bringing together microbiome researchers, nutrition scientists, clinicians, data scientists and machine-learning experts across Europe. The applicant will also contribute to the FlavourFerm project (https://www.flavourferm.eu/), developing bioinformatic pipelines for fermented food microbiomes. Close interactions with consortium partners are encouraged through workshops and consortium meetings.
The ideal candidate is experienced in bioinformatics, (meta)genomic data analysis and microbiology. Specifically, knowledge in analysing microbial communities, machine learning, AI, statistical modelling and comparative genomics will be of benefit. Experience with large-scale or multi-omics datasets, R or Python, and reproducible computational workflows would also be advantageous. Specialised skills will be developed through mentorship and collaboration.
Long-term, this research will contribute to understanding how diet and the gut microbiome interact to influence mental wellbeing, while developing computational approaches for translating complex microbiome and nutrition data into robust, generalisable biological insights.
The post holder will work closely with other group members, the line managers Prof Falk Hildebrand and Dr Maria Traka, as well as key collaborators from the Traka group at QIB, the Jozef Stefan Institute, U Cork, U Wageningen, Austrian Institute of Technology, among other NUTRIMIND and FlavourFerm partners.
The applicant is expected to help in the supervision of junior group members and to represent the group at trainings and will have direct access to established in vitro fermentation models, advanced sequencing facilities, state-of-the-art QIB laboratories, local food databanks with integrated AI systems, as well as one of UK’s largest HPC cluster.
Requirement Importance PhD in Bioinformatics, Population Genetics, Ecology or Microbiology, or equivalent experience Essential
Requirement Importance Programming skills (python or R, bash) Essential Ability to work independently on research projects Essential Initiative and a desire to learn, to innovate, and to move out of their comfort zone Essential Ability to troubleshoot and solve problems Essential
Requirement Importance Machine learning/AI knowledge Essential Proven track-record of research output at the appropriate level Essential Familiarity with microbiome-related bioinformatics (metagenomics/-bolomics) Essential Statistical analysis & visualization microbiomes Desirable Statistical mediation analysis Desirable Experience of grant writing Desirable Lab upkeep (supervision, grant writing) Desirable
Requirement Importance Excellent communication skills, both written and oral Essential Excellent interpersonal skills, with the ability to work alone and as part of a team Essential Reliable and conscientious Essential Promotes and strives for continuous improvement Essential
Requirement Importance Attention to detail Essential Promotes equality and values diversity Essential Willingness to embrace the expected values and behaviours of all staff at the Institute, ensuring it is a great place to work Essential Able to present a positive image of self and the Institute, promoting both the international reputation and public engagement aims of the Institute Essential Willingness to work outside standard working hours when required Essential Willingness to undertake any necessary training for the role Essential
The Quadram Institute is at the forefront of a new interface between food science, gut biology and health, developing solutions to worldwide challenges in food-related disease and human health.
We are engaged in fundamental and translational food and health research, alongside clinical studies, endoscopy and industry, working together to become a leading international hub for food and health research, combining scientific excellence and clinical expertise, delivering impacts on patient care and accelerating innovation.
The Quadram Institute is a diverse and multicultural scientific community. We thrive on our international and European links, appointing staff from across the world. Any candidate who would like further information on current or anticipated immigration requirements can contact the HR Team on +44 (0)1603 450888 or nbi.recruitment@nbi.ac.uk.
The Hildebrand group uses metagenomics to research the diversity, community interactions, and evolution of microbes in communities. Currently the group has projects in both human associated and environmental (food, soil) microbiomes, for which custom bioinformatic solutions are developed in-house to enable a wholistic microbiome exploration (e.g. doi:10.1111/1462-2920.15314). The group explored the limits of using strain-resolved metagenomics for medical gains, and to understand microbiome structures (doi: 10.1016/j.chom.2023.05.024 , 10.1101/2024.09.09.24313184 , 10.1016/j.chom.2021.05.008). See https://github.com/falklab for group information.
The Quadram Institute is based on the Norwich Research Park, which is one of the largest single-site concentrations of research in food, genomics, and health in Europe. The Park hosts 4 BBSRC Institutes (including Quadram), 17,000 students, 3,000 full-time researchers and clinicians, and over 115 companies. The outstanding reputation of our researchers is internationally recognised, with 10 of the Park's scientists in the top 1% of the "most highly cited" in the world.
Postdoctoral Research Scientist (Data Science) Applications are invited for a Postdoctoral Research Scientist position in the Hildebrand group at the Quadram Institute Bioscience (QIB), Norwich, UK. Background Poor mental health is a major global health challenge, and growing evidence points to interactions between diet, lifestyle, the gut microbiome and mental wellbeing. This project will investigate their interaction using large-scale cohort data. The role Within NUTRIMIND ( https://cordis.europa.eu/project/id/101287404 ) standardised data from >20 European cohorts is being generated, covering dietary intake, metagenomics, metabolomics and mental-health measures. The successful applicant will integrate and analysing these datasets to identify reproducible relationships between data layers, including: