项目介绍

2025年5月17日

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).

Your qualities

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.

Our offer

• 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 external link 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 external link, leave schemes and schemes for sports and cultural activities external link, 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 external link.

About us

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

Working at the Faculty of Science external link means bringing together inspiring people across disciplines and with a variety of perspectives and backgrounds. The Faculty external link has six departments: Biology, Pharmaceutical Sciences, Information & Computing Sciences, Physics, Chemistry and Mathematics. Together, we external link work on excellent research and inspiring education. We do so, driven by curiosity and supported by outstanding infrastructure. Visit us on LinkedIn external link and discover how you can become part of our community.

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 external link (Department of Biology) and the Artificial Intelligence Technology for Life (AIT4Life) chair group external link (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 external link (NPEC) and we anticipate extensive use of the facility within this project.

More information

For more information, please contact Dr. Ronnie de Jonge external link at r.dejonge@uu.nl.

Do you have a question about the application procedure? Please send an email to science.recruitment@uu.nl.

Apply now

As Utrecht University, we want to be a home external link for everyone. We value staff with diverse backgrounds, perspectives and identities, including cultural, religious or ethnic background, gender, sexual orientation, disability or age. We strive to create a safe and inclusive environment in which everyone can flourish and contribute.

If you are enthusiastic about this position, just apply via the ‘apply now’ button. Please enclose:

• your motivation letter, in which you describe a significant research challenge you faced during your Master’s thesis or coursework, and how you approached resolving it;
• your curriculum vitae;
• the names, telephone numbers, and email addresses of at least two referees.

If this specific opportunity isn’t for you, but you know someone else who may be interested, please forward this vacancy to them.

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The application deadline is 16 June 2025.