PhD Position on Decoding Plant Microbiota–Host Specificity Using AI

In this MICROP project, we aim to develop predictive AI models to decode plant–microbiota host specificity, with the ultimate goal of forecasting the success of microbial introductions—ranging from single bioinoculants to complex synthetic microbial communities (SynComs). Join us as PhD candidate and contribute to a multidisciplinary effort at the intersection of microbiome ecology, genomics, and data science.

Your job
The project addresses one of the key challenges in microbiome engineering: the unpredictable colonisation and performance of bioinoculants in different plant species and soil environments. Despite the deterministic nature of root microbiome assembly across plant-soil systems, we still lack a fundamental understanding of the principles guiding host-specific microbial recruitment and function. This knowledge gap hampers the reliable deployment of microbial products in agriculture.

In this PhD project, you will integrate high-throughput experimental data from diverse plant species (across multiple families), SynComs from the MICROP culture collection, and advanced phenotyping from the Netherlands Plant Eco-phenotyping Centre (NPEC). You will conduct large-scale SynCom experiments, perform root microbiome profiling through shotgun metagenomics, and apply AI approaches to model microbiome assembly and microbial host specificity. Your work will contribute to identifying both microbial traits and plant genetic components that govern microbiome recruitment and function.

The PhD project is structured around four main goals:

1. Selection of representative plant species across families for SynCom experiments based on phase-I MICROP microbiome data.
2. Experimental inoculation of plants with complex SynComs and detailed phenotyping of plant growth and root microbiomes.
3. Development and validation of AI models to predict microbial colonization outcomes based on plant genetics and microbiome traits.
4. Integration of plant and microbial genomic data to refine phylogeny-aware host-microbiome specificity signatures.

The outcomes of this project will deliver a fundamental understanding of microbiota-host specificity and lay the groundwork for predictive, data-driven bioinoculant design. This will ultimately support the development of robust crop-microbiome engineering strategies for sustainable agriculture.

As a PhD candidate in this project, you will:

• execute the MICROP Phase-II project "Decoding Plant–Microbiota Host Specificity Using AI," working with a multidisciplinary team at the Plant-Microbe Interactions research group at Utrecht University and the broader MICROP team at institutes across the Netherlands;
• conduct experiments using a variety of plant species and SynComs to investigate root microbiome assembly and its host-specific dynamics;
• perform wet-lab experiments including microbial inoculation, root microbiome profiling (shotgun metagenomics), and phenotyping of plant growth and microbial colonisation;
• collaborate with data scientists to develop and validate AI-based models that predict microbiome assembly and microbial host specificity;
• have the opportunity to explore innovative directions in plant-microbiome research and contribute to the development of new microbiome engineering strategies;
• contribute to the training of BSc and MSc students involved in related experimental and computational work (~15% of your time).

We are looking for a highly motivated and creative PhD candidate to join us in this unique project.

You possess:

• an MSc in plant and/or microbial biology, plant biotechnology, or any related field in plant/microbe science;
• proficiency in both written and spoken English;
• demonstrable communication and organisational skills, with the ability to manage interdisciplinary tasks;
• a proactive, curious attitude and a knack for generating creative ideas to advance research.

You are:

• intrinsically motivated with a proactive attitude towards research and problem solving;
• a collaborative team player who thrives in an interdisciplinary research environment;
• resilient in the face of challenges, with a creative approach to overcoming obstacles;
• skilled in practical lab work, particularly in plant biology and microbiology, and have demonstrated experience with bioinformatics and/or machine learning;
• eager to gain further expertise in bioinformatics and the analysis of large 'omics data sets, including metagenomics and transcriptomics and advanced AI methodologies;
• interested in phenotyping techniques, such as automated root and shoot analysis.

• A position for 18 months, with an extension to a total of four years upon successful assessment;
• a working week of 38 hours and a gross monthly salary between €2,901 and €3,707 (salary scale P under the Collective Labour Agreement for Dutch Universities (CAO NU));
• 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 laid down in the CAO NU, Utrecht University has a number of schemes and facilities of its own for employees. This includes schemes facilitating professional development, leave schemes and schemes for sports and cultural activities, as well as discounts on software and other IT products. We also offer access to additional employee benefits through our Terms of Employment Options Model. In this way, we encourage our employees to continue to invest in their growth. For more information, please visit Working at Utrecht University.

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This position is within the interdisciplinary research team led by Dr. Ronnie de Jonge which is embedded within the Plant-Microbe Interactions (PMI) chair group (Department of Biology) and the Artificial Intelligence Technology for Life (AIT4Life) chair group (Department of Information and Computing Sciences, and Department of Biology). Both departments are part of the Faculty of Science of Utrecht University. Dr. De Jonge’s team, specifically, emphasises on developing and leveraging high-throughput methodologies, implementing innovative ‘omics solutions, performing data analysis, and training computer models to characterise plant-microbiome interactions. We have strong ties to the Netherlands Plant-Ecophenotyping Centre (NPEC) and we anticipate extensive use of the facility within this project.