Jobs

Job description 

We are seeking two highly motivated postdoctoral researchers to join our research team based in the Van de Peer lab, under the supervision of Prof. Dr. Zhen Li, Assistant Professor at Ghent University/VIB Staff Scientist.

The two positions are funded through an FWO research grant awarded to Prof. Dr. Zhen Li and will focus on the evolutionary significance of gene loss in shaping plant adaptation, speciation, and biological innovations. 

This project aims to redefine our understanding of gene loss alongside gene gain in plant evolution, focusing on developing novel genomic approaches to analyze gene loss across diverse plant lineages and their evolutionary consequences. To this end, the initial focus will be developing a novel genomic approach integrating homolog identification and multiple genome alignment to identify gene loss in plants and investigating its links with species divergence and adaptive traits in various plant clades, e.g., Orchidaceae, Poaceae, Solanaceae, Brassicaceae, and Fagales.

Research Focus

While the two positions will work closely together and share many responsibilities, they have slightly different initial focus areas:

Position 1: Computational Genomics and Method Development

• Develop novel genomic frameworks for detecting gene loss in plant genomes
• Implement approaches to distinguish DNA deletion from pseudogenization
• Analysis of gene loss patterns across diverse plant lineages
• Explore graph-based algorithms for multiple genome alignment and ancestral karyotype reconstruction

Position 2: Evolutionary Analysis and Network Biology

• Analysis of cross-species/-ecotype co-expression and gene regulatory networks
• Study of molecular mechanisms underlying how gene loss affects biological network rewiring
• Integration of phenotypic data with omics analysis
• Explore machine learning and network analysis methods

Profile

Essential 

• A PhD in Bioinformatics, Computational Biology, Evolutionary Biology, or related fields
• Strong programming skills in Python, R, and experience with Linux environments
• Demonstrated experience in processing and analyzing large-scale genomic and transcriptomic datasets
• Strong publication track record in evolutionary biology and/or network biology
• Excellent communication skills and fluency in English (written and verbal)
• Willingness to learn new methods and tackle scientific challenges with creativity and rigor
• A good balance between working in a team and working independently
• You are an enthused team player who is open and respectful to other cultures, opinions and ways of life 

Desirable but not required 

• Experience with machine learning frameworks (e.g., PyTorch, Tensorflow, Keras)
• Experience with graph algorithms and network analysis
• Experience with explainable AI (e.g., SHAP)
• Experience with high-performance computing and software containers
• Experience with software development tools (e.g., git, Nextflow) 

We offer

The VIB-UGent Center for Plant Systems Biology is a world-leading science institution in Ghent, Belgium. Ghent University is among the top 100 global universities according to several international rankings.

• A fully funded, full-time postdoctoral position for one year (with possibility of a two-year extension after positive evaluation) in a stimulating and supportive international research environment
• Access to state-of-the-art tools and computational infrastructure, including CPU/GPU clusters
• Opportunity to contribute to cutting-edge research in plant evolution and genomics
• Support for attending international conference and developing professional networks
• Comprehensive training in academic, technical, and career skills through VIB and Ghent University
•  The position appointment start date is as soon as possible. 

How to apply? 

Motivated candidates are asked to apply online via the VIB application procedure.

A complete application file (English) should contain the following documents:

• A cover/motivation letter (max. 2 pages) stating career goals, experience, and how these relate to your preferred position 1 and/or 2
• A detailed CV, including a list of your scientific publications
• Contact information of at least two academic references 

For more information or inquiries about the position, mail to zhen.li@psb.vib-ugent.be

We are currently looking for a talented PhD student in the context of the FWO-SBO funded project ‘MATCHMAKING: Unveiling Optimal Soybean-Rhizobia Matches in Northwestern European Environments’ (4-year position). This PhD position is a shared position between the VIB-UGent Center for Plant Systems Biology and ILVO.

The VIB-UGent Center for Plant Systems Biology (PSB, www.psb.vib-ugent.be) is a world-leading plant science institute with the mission to integrate genetics, genomics and computational biology to unravel the biology of plants and to improve the sustainability and climate resilience of crops. The Maere lab at PSB (http://www.maerelab.be) is active in the fields of computational biology, systems biology and evolutionary genomics. Current research topics include testing a novel experimental setup (single-plant omics) to unravel the molecular wiring of plant phenotypes under field conditions, studying dosage balance-sensitive genes in plants, and investigating factors affecting soybean yield and rhizobial inoculant performance in the field.

The institute for Agriculture, Fisheries and Food Research (ILVO) is a multidisciplinary research institute of the Flemish government with focus on research and innovation in sustainable agriculture, horticulture, fisheries and food business. Positive, proactive, professional, working together and being an example are the 5 values that guide ILVO employees in the way they approach their work. At the Plant sciences unit we perform research in the field of genetics and breeding, agronomy of (new) crops, circularity and the efficient use of resources. We also try to understand the processes in the soil. These are key ways to optimize the eco- and environmental system around the plant and thus reduce the environmental impact of agriculture. Sustainable cultivation systems and techniques that promote agrobiodiversity are tested and promoted. Well-equipped research greenhouses, laboratory and trial field infrastructure with extensive machinery, a unique composting site and several rain-out shelters for drought research are key assets.

Project description

Soybean is not only a major source of protein for food and feed worldwide but also, by interaction with nitrogen (N)-fixing rhizobia in the root nodules, a sustainable alternative for N fertilizers. The growing global demand for plant-based protein, coupled with the need to reduce reliance on imports from deforestation-prone areas, drives expansion of soybean cultivation into higher latitudes. Indeed, to meet European demands for sustainable soybean products, production must extend to north-western regions of the continent. However, existing soybean varieties and commercial rhizobia inoculants are not adapted to these environmental conditions, resulting in insufficient nodulation and consequently inconsistent high-protein bean yields. 

Our goal is to identify soybean-rhizobia matches that are suitable for Northwestern Europe, building on a set of endemic rhizobia identified in a previous project. We will achieve this through in-field screening and detailed assessment of the performance of a large set of soybean-rhizobia combinations under adverse environmental conditions. Our research will uncover the genomic regions governing efficient nodulation through GWAS analysis in soybeans and explore the potential of native plant growth-promoting rhizobacteria (PGPR) as helper strains. Additionally, we will conduct comprehensive analyses of efficient interactions using (meta)transcriptomics in nodules. Our project will pave the way for sustainable high-protein soybean production in Northwestern Europe, with the potential to benefit high-latitude regions worldwide.

You will be involved in the setup and execution of the field trials and the analysis of the resulting data (e.g. GWAS, linear mixed models), and in the construction of machine learning models on single-plant omics data to unravel how plant, rhizobial and PGPR gene expression in soybean nodules influence the performance of the top soybean-rhizobia combination in the field.

Profile

• You have a MSc degree in Bioengineering, Biotechnology, Computational Biology or Bioinformatics.
• You have a solid background in plant biology, statistical data analysis, machine learning, R and Python programming
• Experience with plant field trials is a plus
• You are fluent in English (spoken and written).
• You can work in a team as well as independently.
• You are meticulous, well-organized, responsible and self-critical.
• You take pride in delivering high-quality work.
• You have a passion for science and you can think outside the box.

We offer

• An exciting work environment in both a top basic research institute (VIB) and a top applied research institute (ILVO).
• The opportunity to be part of a dynamic, interdisciplinary and international team.
• A challenging project with high societal value.
• Ample opportunity to learn new skills.
• An attractive salary.

Please complete the online application procedure and include a detailed CV, a letter of motivation and the contact details of two referees. Applications are accepted until the position is filled. 

For more information, contact Steven Maere (steven.maere@psb.vib-ugent.be) and Hilde Muylle (hilde.muylle@ilvo.vlaanderen.be).