Statistician specialising in Applied Spatial Statistics

Biomathematics & Statistics Scotland (BioSS) seeks a statistician with an interest in working on spatial statistics in applied and methodological research relating to the impacts of offshore renewable energy on seabirds and marine mammals. BioSS is legally part of The James Hutton Institute, a member of the SEFARI (Scottish Environment, Food and Agriculture Research Institutes) collective, and offers a stimulating working environment, with 50 staff and students at four locations, collaborating on applications in environmental science & ecology, plant & crop science, animal health & welfare, and human health & nutrition.

The UK government is committed to reaching net zero emissions by 2050. In the power generation sector, progress in switching to renewable energy has accelerated in recent years, with renewables generating more electricity in the UK than fossil fuels since 2020. Legislation protecting the marine environment requires that offshore renewable energy devices are delivered in a sustainable manner. Offshore renewable developments have the potential to impact protected seabird and marine mammal populations, principally from collisions with turbine blades, displacement from important habitat, barrier effects to movement and noise disturbance.

The Offshore Renewables Group in BioSS carries out quantitative research on the impacts of offshore renewable energy on seabirds and marine mammals in collaboration with the UK Centre for Ecology and Hydrology (UKCEH) and others. This position offers the opportunity to work in a small team within three consortia on exciting long-term projects, which have substantial spatial statistics components. This is an opportunity to be involved in developing interesting statistical approaches using novel ecological data to solve real-world problems relating to the impacts of offshore renewables on seabirds.

1. Funded by the Offshore Wind Evidence and Change Programme (OWEC), Predators and Prey Around Renewable Energy Devices (PrePARED) is providing critical insight into cumulative effects from large scale developments for key species. Surveys are collecting concurrent data to characterise and quantify relationships between predators and prey in the context of a changing environment. Spatial models are being developed using mgcv and inlabru approaches to produce species distributions with uncertainty.
2. Funded by NERC, the Ecosystem Change, Offshore Wind, Net Gain and Seabirds (ECOWINGS) project seeks to transform the existing evidence base on cumulative effects for seabird species, establish pathways for marine net gain, and account for climate change. Collecting fine-scale contemporaneous data using innovative drone technologies, we are developing spatio-temporal models to investigate interspecific competition, the impact of wind farms on predator-prey interactions, and whether species may habituate to developments post construction.
3. Funded by the Evidence for Offshore Wind programme, the Foraging Ecology of Guillemots and Razorbills in the Non-Breeding Season project will seek to understand interannual variation in foraging behaviour and energetics. Data collection is being carried out using geolocator tags and the collection of moult feathers for isotope analysis. We will be constructing spatial models and integrating isoscapes to investigate variation in seabird distribution and foraging areas in the non-breeding season.

This position will also have links to the Statistical Methodology Theme group in BioSS, and the role will provide the opportunity to work with scientists developing novel spatial statistics approaches.

Job description

Purpose of the post

• Work on collaborative research projects providing statistical and quantitative expertise, with a focus on applications in spatial ecology.
• Contribute to revenue generation through completion of existing projects, providing statistical support to BioSS tendering for research opportunities in ecological statistics.
• Develop research in spatial statistical methodology and applied spatial statistics problems at the interface between statistics and ecology, motivated by quantitative problems encountered in collaborative projects.

Main responsibilities of post

• Collaborate with scientists across interdisciplinary consortia, delivering research specialising in spatial ecological statistics (e.g. spatial modelling, spatio-temporal modelling, data integration, and propagation of uncertainty).
• Develop statistical research relevant to ecological applications in the BioSS statistical methodology research theme.
• Support BioSS deliverables in project consortia including by making presentations, writing reports, producing manuscripts for external peer review, and representing BioSS at internal and external meetings and conferences.
• Work in support of senior BioSS staff in tendering for external funding from the Scottish Government Marine Directorate and other government bodies, UKRI, and industry projects relating to the impacts of offshore renewable energy developments on seabirds and marine mammals, and on delivering the project(s) if successful.

Grade, starting salary, duration, and location

• This post will be offered at Hutton Grade D (statistician, salary £33,595). This is a permanent appointment.
• Location for this post is flexible; the post-holder will be expected to spend some time at the BioSS offices at the University of Edinburgh, but we offer a flexible working approach where a degree of working from home is encouraged; we also have offices in Aberdeen and Dundee which could be made available to the post-holder.

Knowledge, skills and experience

Essential

• A PhD in statistics or another discipline having a substantial quantitative element, or an MSc with commensurate post-qualification development and relevant work experience.
• An ability to communicate with other scientists, understand their analytical challenges and contribute to collaborative statistical research.
• Ability to work independently.
• Ability to work within a team.
• Evidence of willingness to seek funding.
• Good written communicator.
• Willingness and ability to give spoken presentations disseminating technical methods and results to non-quantitative audiences.
• Enthusiasm for collaborative working at the interface of statistics and ecology.
• Enthusiastic about development and application of statistical methods.
• Experience of spatial and/or spatio-temporal modelling.
• General statistical skills, including an understanding in one or more relevant areas of: mixed models, generalised linear models, generalised additive models, spatial modelling, Bayesian statistics, sampling design.
• Ability to handle, process, manipulate, and analyse large data sets.
• Good programming ability in a statistical programming language such as R or Python.

Desirable

• Track record of research and/or collaboration evidenced by scientific papers, preferably in a field relevant to ecological statistics.
• Experience of collaborative working at the interface between statistics and the applied sciences.
• Experience of managing projects and communicating with government and commercial clients.
• Experience working with real data such as: tracking, biologging, accelerometry, aerial/at-sea survey, environmental, mark-recapture, presence/absence, spatial capture-recapture, bioacoustic.
• Experience in developing open-source analytical tools and packages for use by the wider scientific community.
• Evidence of ability to manage and motivate staff.
• Evidence of experience and ability in seeking funding.