Nick Schurch

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Biomathematics and Statistics Scotland
BioSS Office
The James Hutton Institute, Craigiebuckler
ABERDEEN, AB15 8QH, Scotland, UK.

Tel: +44 (0)1224 395126

Nick Schurch


I joined BioSS as the Principal statistician for Ecology and Environmental Science in April 2019, shifting field from bioinformatics. In this position I lead the team of BioSS statisticians embedded at the James Hutton Institute site in Aberdeen, focussing on modelling environmental research data. We collaborate on projects spanning the full range of environmental science, including climate change, water pollution, landscape decision making, environmental eDNA forensics and biodiversity census data. This work includes a strong methodological research focus in the group, including Hidden Markov Models of time-series data, ordinal response modelling, and using Bayesian belief networks for data integration. I did my undergraduate MSci in physics at the University of Bristol, before completing a Ph.D. in Astrophysics at the University of Leicester in 2002. Continuing my PhD work, I completed post-docs/fellowships across three continents studying the X-ray spectra of obscured Seyfert galaxies (Carnegie Mellon University, USA; Durham University, UK; and a UK fellowship for Excellence at the Institute for High Energy Physics in Beijing). In 2009 I changed fields to biology, shifting scales by ~20 orders of magnitude to focus on studying transcription and gene expression as part of the Barton Group at the University of Dundee. I completed several post-docs with the Barton group culminating in a Senior Researcher position leading a small team of experienced bioinformaticians working on data from a wide range of cell biology data and developing several novel open-source bioinformatics tools. Since joining BioSS in 2019 I have been exploring a range of environmental science research areas with a focus on bringing my quantitative computational science experience to bear in an environmental context. I am currently involved in collaborative projects using predictive multivariate models to understand spatial and temporal river temperature data, using Bayesian belief networks to integrate river water quality monitoring data with expert opinion in order to facilitate informed decision making, and leveraging portable 4th-generation long-read sequencing technology for projects in forensic soil science, whole genome sequencing of unculturable fungi, and for soil metagenomics projects that relate soil health to land management decisions. Outside of work, my time is mostly taken up by my young family but when I get a moment or two I use it watching Liverpool FC (I'm a lifelong Liverpool fan despite not coming from Liverpool!), cooking, playing board/video games (especially in VR), photography, skiing, scuba diving, and watching films.

Research Interests

  • Environmental application of portable 4th-generation long-read sequencing technology.
  • Dataset integration.
  • Open science.
  • Bayesian hierarchical models.
  • Publications

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