Exciting Opportunity: Researcher/Senior Researcher in inference and learning for complex stochastic systems; BioSS (Permanent Position)
A unique opportunity to build a career in statistical inference & learning for stochastic processes and complex systems models at BioSS.
The most pressing societal challenges of the first half of the 21st century, including disease and future pandemic threats, climate change, the biodiversity crisis, and building a restorative economy, are systems challenges. This post is an exciting opportunity to join our team developing and applying tools for inference and uncertainty quantification to parameterise, assess and compare models, thus making best use of data to address such challenges. You will have the opportunity to develop and apply state-of-the-art methodology including Bayesian computational approaches to inference for stochastic processes and explore the potential for machine-learning in models for complex and adaptive systems.
The post is an excellent long-term career opportunity offering a stimulating route to build your research portfolio and technical expertise, and to develop skills in inter-disciplinary working across application areas including epidemiology, ecology and agriculture. You will collaborate closely with applied scientists from other leading UK research institutions such as the SEFARI collective (sefari.scot/), the Roslin Institute (www.ed.ac.uk/vet/roslin), UKCEH (www.ceh.ac.uk/) and the EPIC consortium (www.epicscotland.org/).
You will have the chance to advance your career in a supportive environment at BioSS as we continue to grow as a UK centre of quantitative applied research. We want to help you to build a personal portfolio of research, and to develop professionally over the long term, and will actively support you in these objectives. BioSS is eligible to apply for UKRI funding, and we will be keen for the successful applicant to contribute to and ultimately lead proposals. This is a permanent post based at BioSS in Edinburgh, with flexibility to work from other BioSS locations in Dundee or Aberdeen, or remotely from home.
BioSS is legally part of The James Hutton Institute.
More information about BioSS including details of this and other vacancies can be found via https://www.bioss.ac.uk/vacancies
Potential applicants may contact Prof Glenn Marion (glenn.marion@bioss.ac.uk) to discuss this post.

MAIN PURPOSE OF JOB

• Develop, apply and publish methodology including Bayesian approaches to inference for stochastic process models and explore potential for machine-learning in models for complex systems
• Contribute to RESAS SRP and other projects through development of methodologies and collaboration with SEFARI scientists and others
• Ongoing engagement with the objectives, activities and scientists within the EPIC Centre of Expertise, leading to scientific collaborations and the application of novel quantitative tools
• Contribute to revenue generation through completion of funded projects including submitting papers for review and supporting development of project proposals

MAIN DUTIES OF POSTHOLDER

• Develop an area of personal research in methodology for inference for stochastic process models including Bayesian and computationally intensive methods
• Explore applications of machine-learning techniques in models for complex systems
• Use and develop software to enable wider application of methods for inference and learning and contribute to BioSS team activity in these areas
• Support EPIC disease outbreak preparedness through application of relevant methods
• Apply inference & learning methods to real world data and applications across the RESAS portfolio including those relevant to the SRP and the Plant Health Centre
• Represent BioSS at meetings with stakeholders from scientific and non-scientific backgrounds.
• Promote use of methods within the SEFARI collective e.g. through development of collaborations and training courses
• Develop and facilitate research collaborations e.g. with the Roslin institute, SEFARI, UKCEH and others and help to exploit resulting funding opportunities
• Make or support applications for external e.g. UKRI funding and deliver resulting projects