Postdoc Position in AI Foundation Models for Crop Microbiomes

Sport Society; Utrecht; On-site; EUR 41,000 - 64,000; Full time

Benefits offered by this job

Holiday pay

Year-end bonus

Pension scheme

Flexible terms

Job summary

Utrecht University is seeking a Postdoctoral Researcher to lead the development of ARCA, a crop microbiome foundation model, within the NOAH project. You will explore architectures and objectives for sparse, high-dimensional data and translate methods into robust trainable models for pretraining and downstream tasks.

Your work spans deep learning, bioinformatics and microbial ecology, with collaboration across UU, Aarhus, Niab, INRAE and The Hyve.

Qualifications

• PhD in machine learning, artificial intelligence, computational biology, bioinformatics, computer science or related field.
• Strong hands-on experience with deep learning and representation learning (transformers, self-supervised, foundation models, autoencoders).
• Strong Python programming skills and experience with PyTorch, including GPU/HPC training/evaluation.
• Experience with high-dimensional biological, omics, ecological or similarly sparse data; motivation to develop this area.
• Interest in interpretable AI, rigorous benchmarking and reproducible research, with collaboration across AI, bioinformatics, microbiology and crop science.

Responsibilities

• Design, implement and benchmark foundation-model architectures for microbiome data, including transformer-based and MAE approaches.
• Develop representations integrating microbial identity, abundance, genomic/functional info and contextual metadata.
• Define and evaluate self-supervised objectives and embedding strategies; benchmark against baselines.
• Train and evaluate ARCA on large-scale microbiome datasets, addressing sparsity, batch effects, scalability and uncertainty.
• Fine-tune ARCA for microbial root competence and crop-relevant outcomes; iteratively improve via Design-Build-Test-Learn cycles.
• Develop generative ARCA components using autoencoder- or diffusion-based approaches.
• Apply interpretable AI to identify microbial taxa, functions and contextual drivers of predictions.
• Develop reproducible training/evaluation workflows and collaborate to deploy tools beyond research.

Skills

Deep learning

Transformers

Self-supervised learning

Python

PyTorch

GPUs / HPC

Interdisciplinary collaboration

Interpretable AI

Education

PhD in ML / computational biology / bioinformatics

Tools

PyTorch

GPUs / HPC

Job description

Postdoc Position in AI Foundation Models for Crop Microbiomes

Faculty: Faculty of Science Department: Department of Information and Computing Sciences Hours per week: 36 to 40 Application deadline: 16 October 2026

Do you want to develop a foundation model for one of biology’s most complex ecosystems? By joining the European Innovation Council (EIC) Pathfinder project NOAH, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale microbiome and genome data to learn contextual representations of microbes and communities and translate them into predictive models for successful crop microbiome engineering.

Plant-associated microbiomes can strongly influence crop growth, nutrition and resilience, but their behaviour depends on the crop, soil, environment and the surrounding microbial community. NOAH aims to make these context-dependent interactions learnable and predictable. At the centre of the project is ARCA (AI-guided Root microbiome engineering for ClimAte-resilient and nutritious crops), a crop microbiome foundation model trained on large-scale public and newly generated datasets.

As Postdoctoral Researcher in AI, you will take a leading technical role in developing ARCA. You will explore which model architectures and learning objectives work best for sparse, high-dimensional and heterogeneous microbiome data, and turn the selected approaches into robust trainable models. Your work will cover both foundation-model pretraining and downstream predictive and generative applications.

Your main responsibilities are to:

• design, implement and benchmark foundation-model architectures for microbiome data, including transformer-based and masked-autoencoder approaches and relevant architectures adapted from related biological domains;
• develop representations that integrate microbial identity and abundance with genomic or functional information and contextual metadata such as crop genotype, soil and environmental conditions;
• define and evaluate self-supervised learning objectives and embedding strategies, and benchmark their added value against simpler machine-learning baselines;
• train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies and uncertainty in downstream predictions;
• fine-tune ARCA for tasks including microbial root competence and crop-relevant outcomes, and iteratively improve the model using experimental Design-Build-Test-Learn data generated by NOAH partners;
• develop a generative ARCA component, exploring autoencoder- and/or diffusion-based approaches for generating ecologically plausible microbiome configurations;
• apply interpretable and explainable AI approaches to identify microbial taxa, functions and contextual features driving model predictions;
• develop reproducible training and evaluation workflows and work with project partners to make models and associated tools usable beyond the immediate research setting.

You will not work on an isolated AI benchmark. ARCA predictions will be tested experimentally in greenhouse and field settings and the resulting microbiome and crop phenotype data will feed back into model development. This gives you the opportunity to develop new AI methodology while seeing how model predictions perform in a real biological and agricultural system.

In this position you will part of an interdisciplinary research environment spanning the AITechnology for Life

external link groups, with close interaction with bioinformatics, microbial ecology and experimental crop research at the UU and NOAH partners. You will have access to Utrecht University GPU/HPC infrastructure and large, curated microbiome and microbial genome datasets. Additionally, you will collaborate closely with NOAH partners at Aarhus University, Niab, INRAE and The Hyve, including experimental teams that will directly test model predictions.

We are looking for a postdoctoral researcher who enjoys developing methods for complex biological data and working closely with experimental scientists. You meet the following criteria:

• a PhD, or a PhD close to completion, in machine learning, artificial intelligence, computational biology, bioinformatics, computer science or a closely related field;
• strong hands-on experience with deep learning and modern representation learning, preferably including transformers, self-supervised learning, foundation models, autoencoders or related architectures;
• strong programming skills in Python and experience with a deep-learning framework such as PyTorch, including training and evaluating models on GPU/HPC infrastructure;
• experience working with high-dimensional biological, omics, ecological or similarly sparse and heterogeneous data, or a clear motivation to develop this expertise;
• an interest in interpretable AI, rigorous benchmarking and reproducible research, together with the ability to collaborate across AI, bioinformatics, microbiology and crop science.

Experience with microbiome data, microbial genomics, metagenomics or multimodal biological data is an advantage, but is not required if you bring strong machine-learning expertise and are motivated to learn the biology.

Our offer

• a central role in developing ARCA, the core AI technology of the five-year EIC Pathfinder project NOAH;
• a position available from January 2027, initially for 1 year, extended with 3 years after a positive evaluation;
• a gross monthly salary, depending on qualifications and expereince, between €3.706 and €5.760 (salary scale 10 under the Collective Labour Agreement for Dutch Universities (CAO NU)). The salary is based on a 38-hour working week;
• 8% holiday pay and 8.3% year-end bonus;
• a pension scheme, partially paid parental leave and flexible terms of employment based on the CAO NU.

In addition to the terms of employment set out in our collectivelabouragreement, we offer attractiveadditionalbenefits, including opportunities for personal and professional growth , flexibleleavearrangements,and extravacationdays. Through the UU Terms of Employment Options Model, you can tailor your employment package to your needs. In this way, we encourage you to grow in what you do, both in your work and in your development.Read more about our terms of employment .

About us

A better future for everyone. This ambition motivates our scientists in executing their leading research and inspiring teaching. At Utrecht University , the various disciplines collaborate intensively towards major strategic themes . Our focus is on Dynamics of Youth, Institutions for Open Societies, Life Sciences and Pathways to Sustainability. Sharing science, shaping tomorrow .

Working at the Faculty of Science

This position is embedded jointly in the AI Technology for Life group in the Department of Information and Computing Sciences, and the Plant-Microbe Interactions group in the Department of Biology. The research combines machine and deep learning (AI), microbiome biology, microbial genomics, and bioinformatics. You will work closely with Dr Ronnie de Jonge , Prof. Sanne Abeln and researchers across both groups.

You will be part of NOAH, a European EIC Pathfinder consortium coordinated by Utrecht University. NOAH brings together Utrecht University, Aarhus University, Niab, INRAE and The Hyve to develop predictive and generative AI for crop microbiome engineering. The consortium connects AI model development directly to large-scale microbiome data generation, microbial culture collections, greenhouse experiments and field validation.

More information

For more information, please contact Dr Ronnie de Jonge at [e-mail] .

The application deadline is 16 October 2026.