Are you a talented, motivated researcher who is interested in the application of advanced computational techniques to study fundamental genetic processes in plants and fungi? Do you want to develop and apply machine learning approaches in order to study a variety of genomics datasets? Are you able to collaborate with different researchers, such as bioinformaticians, geneticists and plant breeders in order to unravel underlying determinants of meiotic recombination?
The project: you will develop generally applicable methodologies that can be applied to various datasets from different species available at GBU, including plants and fungi. You will analyze the crossover landscapes in these datasets, building on a currently available machine learning model. You will extend the model to incorporate the effect of structural and allelic variation, and you will use the model to analyze the effect of meiotic recombination on genetic variation in genes of interest, in particular disease resistance genes. Task include:
• carrying out computational method development;
• working closely together with researchers from GBU that provide datasets;
• analyzing these datasets and integrating analyses from different species.
The project is funded by the Genome Biology Unit in order to foster collaboration between the various participating groups. An important aspect of this postdoc position will be to organize and stimulate dissemination activities and interactions between the different partners involved. Your main supervisor will be Dr. Aalt-Jan van Dijk in the Bioinformatics group. A broad range of relevant expertise is available in this group, in particular on the development and application of machine learning to biological processes. Other collaborators from the different groups in GBU include Ben Auxier, Yuling Bai, Guusje Bonnema, Klaas Bouwmeester, Paul Fransz, Martina Juranic, Arend van Peer, Sander Peters, Henk Schouten and Eveline Snelders.
The research: meiosis is an essential process to ensure that the correct number of chromosomes is retained during sexual reproduction. It involves the formation of recombination events or crossovers between homologous chromosomes. These crossovers do not occur randomly over the chromosome. A better understanding of what drives their preferential localization is of fundamental scientific interest, and will lead to great practical benefits for plant breeding. We are looking for a two-year postdoc to apply machine learning to study various available datasets on plant and fungal meiotic recombination. The position is part of a collaboration between four research groups (Bioinformatics, Biosystematics, Genetics, and Plant Breeding) in the Genome Biology Unit (GBU) of Wageningen University & Research.
Your profile demonstrates:
- a PhD degree in a relevant field, e.g. bioinformatics, computational biology, biosystematics, plant sciences, plant breeding or genetics;
- hands-on experience and interest in developing technologies towards analyzing genome datasets from various species;
- the motivation to initiate and organize collaborations between the different research groups involved in the project;
- excellent analytical, communication and scientific writing skills.
In addition, given the importance of both computational skills as well as understanding of relevant biology, candidates matching one of the following two points will be selected with priority:
- proven experience with machine learning and interest in applying these technologies to biological processes;
- knowledge of molecular processes such as meiotic recombination and interest in applying machine learning to study such processes.
Please only apply through the online webform at www.wur.nl/en/vacancy/postdoc-machine-learning-towards-studying-meiotic-recombination.htm