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for the 2026 intake. Interview by the respective clusters for shortlisted applicants will be conducted throughout the year. Final Interview by Panel for shortlisted applicants will take place from March to April
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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
Integration of phenotypic data with omics analysis Explore machine learning and network analysis methods Profile Essential A PhD in Bioinformatics, Computational Biology, Evolutionary Biology
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propulsion, engine clustering, low structural ratio and low-cost system-integrated avionics; develop mathematical models relating to the cryogenic liquid engines and their components to be applied
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them in this position: distributed and cluster computing, with a specific focus on PySpark, working with large tabular data with python/R, basic principles of natural language processing and their
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computing clusters and analysis of transcriptomics and genomics datasets. Desirable Requirements Experience in single-cell and spatial OMICS data analysis. Development of ShinyApps and
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for both ligands and novel coordination clusters under a range of conditions. Candidates must also be able to work well independently but also as part of a large team, regularly reporting on progress
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or more of the following: Experience with large-scale molecular dynamics (MD) simulations using software such as LAMMPS. Experience in handling and data analysis generated from multi-million to multi
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and curiosity-driven working culture. Research activities are carried out in research groups organised into three multidisciplinary research programmes. For further information please visit https
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are particularly interested in applicants who have experience with methods and analysis of big data, especially genetics and genomics datasets. However, we will consider qualified applicants with experience in other
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uncovering companion biomarkers for precision treatment using cutting-edge bioinformatics and proteomics approaches. You will analyze bulk and clonal protein expression data from large melanoma cohorts