"Postdoc position on federated learning and integrative omics for antibiotic discovery"

Antibiotics have made a huge contribution to the extension of human lifespan. However, antimicrobial resistance (AMR) is spreading rapidly, making existing antibiotics ineffective. At the same time, the high rediscovery rate of antibiotics and limited commercial incentives frustrate antibiotic R&D. The co-occurring problems of AMR and nearly empty discovery pipelines form an impending crisis, often referred to as the ‘silent pandemic’. Most of the antibiotics in clinical use are natural products derived from microorganisms. Large-scale bacterial genome sequencing has revealed that most the natural product structural classes have not yet been fully characterized. Similarly, most metabolites observed in mass-spectrometric data cannot be fully dereplicated. The key question we seek to answer in this project is how to effectively prioritize the millions of unknown biosynthetic gene clusters and metabolite features for the discovery of new antimicrobials through predicting structural and functional features of metabolites from genomic and mass-spectrometric data. A major challenge in current antibiotic drug discovery is that genomic, metabolomic, bioactivity and culturing data is scattered across a wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources without sharing the data has been an area of active research in various fields. This recent machine learning methodology is generally referred to as federated learning.

In this position, you will set up a federated learning infrastructure and develop multimodal machine learning methods to predict connections between mass spectra, biosynthetic gene clusters, molecular structures, and biological activities, in collaboration with national and international partners. In addition, there will be plenty of opportunities to collaborate with fellow postdocs and PhD students at the Bioinformatics chair group in Wageningen. You will be part of the KIC PRIORITY consortium that aims to improve the antibiotics discovery pipeline. With several academic and industrial partners involved, your work will form the input for other work packages that include the elicitation, expression, and structural elucidation of novel antibiotic candidates, as well as lead generation and optimization. In the end, the research is projected to lead to a new globally accessible infrastructure and new algorithms for multimodal mining of biosynthetic diversity, as well as novel lead compounds that can be taken up by consortium partners to attain concrete societal impact.

Your duties and responsibilities include:

• Setting up federated learning infrastructure to connect omics and phenotypic datasets across public and private international institutes
• Development of machine learning (including deep learning) algorithms to predict links between gene clusters and metabolites, and to predict antimicrobial activities associated with these
• Collaboration with international partners

Your team: You will work within the Bioinformatics chair group at Wageningen University. The expertise of the research groups of Probst, van der Hooft and Medema within the Bioinformatics chair group lie in artificial intelligence, cheminformatics and computational genome and metabolome mining.

Requirements: You are an ambitious, enthusiastic team player and result-driven scientist with: a strong academic record (PhD) in bioinformatics, computer science or a related subject; affinity with the study of metabolism and analysis of omics data; proven proficiency in programming; experience with machine learning and, ideally, federated and/or deep learning methods; intermediate to high level of statistical and mathematical skills; excellent oral and written communication skills in English. In our international working environment there is an increasing amount of communication in English. For this position it is about language level C1.

You will work here: Wageningen UR has a global reputation for its ground-breaking research and innovative education in the fields of food and health, sustainable agrosystems, the rural environment, and societal development - all of which make an essential contribution to the quality of life, i.e. the supply of safe and healthy food and drink, on the one hand, and the chance to live, work and play in a balanced ecosystem with a large variety of plants and animals on the other. Within Wageningen UR, the Plant Sciences Group (PSG) brings together science education and scientific, strategic and applied research in the fields of biology, of plants in relation to their environment, of plant-related organisms and of plant production. This project will be carried out at the Bioinformatics Group within PSG. This research group focuses on fundamental and applied bioinformatics research in the green life sciences. In particular, they develop and apply novel computational methods for the analysis and integration of –omics data. The group has a strong track record in (integrative) computational omics analysis, algorithm development, machine learning and scientific data infrastructure. There are many national and international collaborations with researchers studying plant and microbe metabolism, biotechnology and genomics.

Conditions of employment: Wageningen University & Research offers excellent terms of employment. A few highlights from our Collective Labour Agreement include: sabbatical leave, study leave, and partially paid parental leave; working hours that can be discussed and arranged so that they allow for the best possible work-life balance; there is a strong focus on vitality and you can make use of the sports facilities available on campus for a small fee; a fixed year-end bonus of 8.3%; excellent pension scheme. In addition to these first-rate employee benefits, you will of course receive a good salary. Depending on your experience, we offer a competitive gross salary of between € 3.546,- and € 5538,- for a full-time working week of 38 hours, in accordance with the Collective Labour Agreements for Dutch Universities (CAO-NU) (scale 10). Additionally, a contract for 0.8 or 0.9 FTE can be discussed. As for your contract, we initially offer a one-year contract that can be extended by another two years on good performance.