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Closing date: 17th September 2026 Background Applications are invited for a 2 year full-time Post Doctoral Research Fellow with bioinformatics expertise to contribute to the analysis of large datasets
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on application materials. Required Qualifications: • Current enrollment in an undergraduate or graduate degree program in computer science, data science, biology, genetics, bioinformatics, or a related field
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profile Applicants should hold a PhD in ecology, molecular ecology, genomics, microbiology, bioinformatics, environmental science, or a related discipline. We seek candidates with experience in several
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sequencing library preparation is desirable. Previous computational experience is not essential, as training and support in bioinformatic and single-cell data analysis will be provided. The position would
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spectrometry-based proteomics, including data deposition and retrieval through the PRIDE (Proteomics Identifications) database, and in bioinformatic data analysis, including pathway and ontology enrichment (ORA
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advanced multi-omic / bioinformatics integrations to model and dissect human diseases. Our lab focuses heavily on inventing and refining Next Approach Methodologies (NAMs)—specifically, brain organoids, Blood
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the development of an independent research program. Minimum Education Required Doctoral degree or equivalent terminal degree in biostatistics, statistical genetics, genetic epidemiology, bioinformatics
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, bioinformatics, epigenomics or cell metabolism is desirable. Full criteria are set out in the Further Particulars. This is a Grade 7 Research Associate post. Candidates who have submitted their PhD but not yet
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experience in the study of gene regulation, hematopoiesis, bioinformatics analysis, and/or use of mouse models is preferred. Interested candidates should send their Curriculum Vitae and a cover letter
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established and emerging bioinformatics, statistical modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics