Machine Learning Scientist - Westerdijk Fungal Biodiversity Institute - Utrecht

The challenge

For this position, we seek a machine learning expert ready to tackle one of the most diverse and under-explored datasets in biology.

Our institute maintains one of the world’s richest fungal strain collections, and we are generating large in-house datasets that are multi-modal and paired with each sample:

• High-resolution images (morphology, microscopy);
• DNA sequences (barcodes, full genomes, transcriptomes); and
• Phenotypic and biochemical profiles (structured/tabular data).

Our datasets are not just big — they’re continuously expanding with tens of thousands of strains, each with matched image, sequence, and phenotype data. On top of our in-house resources, you’ll integrate with external repositories (e.g., GEO, MycoCosm, MIRRI-ERIC, etc.), giving you access to even more omics and microbial data.

You will design the next generation of AI algorithms to:

• Integrate image, sequence, and tabular data in a single modeling framework;
• Build correlation networks across biological and environmental variables;
• Develop scalable pipelines for classification, anomaly detection, and clustering
• Enable rapid, automated species identification at a speed the field has never seen; and
• Create a robust data infrastructure to connect internal and external datasets

“This means you can train and evaluate models on truly linked biological datasets — a rare opportunity in applied AI.”

Why This Matters?

Globally, only a tiny fraction of fungal species has been described. Your work will directly accelerate discovery — compressing what is currently a slow, manual process into an AI-assisted, high-throughput pipeline. By developing AI tools to discover species boundaries, rapidly and accurately identify species, detect pathogens, and predict traits, your work will directly contribute to protecting food security, preventing invasive species, improving early disease diagnosis, and enabling new biotechnological applications. You will not just be advancing AI research — you will deliver solutions with real societal and environmental impact.

 Who Are You

You are a curious, enthusiastic, and independent working bioinformatician with experience in machine learning and developing deep learning AI pipelines for complex biological data.

Furthermore, you possess:

• A PhD in Computing Science, AI, or Bioinformatics;
• Demonstrable experience with programming language and deep learning libraries (e.g., Python with Keras, PyTorch, and/or TensorFlow);
• Demonstratable expertise in developing and implementing AI methodology;
• Experience with metadata handling, archiving, and FAIR data principles;
• Strong attention to detail, organizational skills, and communication skills;
• Fluency in English; Dutch language skills are a plus;
• Background in genomics or mycology is beneficial but not required.

"Working at the Westerdijk Fungal Biodiversity Institute — a world of knowledge, innovation, and growth"

What We Are Offering

• A full-time postdoc position in a leading fungal biodiversity institute;
• Opportunities to participate in internal training for soft skills development;
• To become part a cross-disciplinary bioinformatics cluster that blends existing biology expertise with new AI skills;
• Working as team to build a central data platform, integrating analysis pipelines into daily workflows, and making curated datasets openly available to the global research community;
• A chance to advance AI research and deliver solutions with real societal and environmental impact.

“You bring the technical expertise — we will provide the data, the domain experts,

and the chance to make a measurable impact in both AI and science.”

Application procedure

Need more information? No problem at all. The director of the Westerdijk Fungal Biodiversity Institute will be happy to tell you more! He can be reached via email at: w.meyer@wi.knaw.nl

About the Westerdijk Institute

The Westerdijk Fungal Biodiversity Institute is a research institute of the Royal Netherlands Academy of Arts and Sciences located at Utrecht Science Park. Our researchers study biodiversity across the entire spectrum of the fungal kingdom. They develop and use a wide range of techniques to map the biology, ecology, and genetic information of fungi.

With a unique collection of more than 100,000 living fungal strains, yeasts and bacteria, we are at the forefront of mycological research worldwide. We are a center of expertise for mycology and contribute to solving socially relevant issues in the areas of health, agriculture and industry.

Our relatively small, internationally diverse institute has 50 employees and a similar number of guest researchers.
Additional information is available on our website: www.wi.knaw.nl.

Terms of employment
Depending on education and experience the minimum salary is 4.728 and the maximum salary is 6.433 gross per month for a full-time appointment (scale 11 cao Nederlandse Universiteiten/KNAW). This is exclusive of 8% vacation allowance, 8.3% year-end bonus, travel allowance, internet allowance, home working allowance and pension accrual with ABP.

The KNAW offers its staff an excellent package of secondary benefits. A package that meets the different needs of employees depending on their stage of life, lifestyle or career ambitions. For example, by working an extra two hours a week, it is possible to increase the number of days off from 29 to 41 days a year (with full-time employment). 

For a complete overview of the terms of employment, please refer to the web page: werken bij de KNAW.

Applying for a Certificate of Good Conduct can be part of the employment procedure.

Diversity & Inclusion
The KNAW considers a working environment in which everyone feels welcome and appreciated of great importance. A working environment in which attention is paid to individual quality and where development opportunities are paramount. Together we strive for an inclusive culture in which we embrace differences. We would therefore like to invite candidates who want to contribute to this through their background and experience. In the event of equal suitability, preference will be given to the candidate who thus enhances diversity within the Academy.

We will not respond to any supplier enquiries based on this job advertisement.