Researcher/ Senior Researcher in inference and learning for complex stochastic systems

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 on the detail of the posts and the post holders can be found at https://www.bioss.ac.uk/vacancies

Potential applicants may contact Prof Glenn Marion () 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

Education/Experience/Skills

Essential

• PhD (or MSc with compensatory experience) in a quantitative discipline with substantial computational and statistical/appropriate mathematical components.  Suitable candidates in the final stages of a PhD programme may also qualify.
• Experience of methodological development and scientific collaborations in statistical inference for stochastic processes
• Track record of publications underpinned by quantitative methods (not essential for Band D appointments)
• Evidence of contribution to funding proposals or a strong track record of publications underpinned by quantitative methods (not essential for Band D appointments)
• Evidence of engagement in the application of modern quantitative methods to address scientific problems (not essential for Band D appointments)
• Ability to interact positively, effectively and confidently with collaborators in formal and informal situations
• Enthusiasm for development and application of quantitative methods, and for collaborating with applied scientists in a range of scientific areas.
• Ability to work independently
• Good programming ability to handle large data sets and deploy computational and statistical bioinformatics techniques
• Excellent written communicator
• Willingness and ability to give verbal presentations presenting technical methods and results to non-quantitative audiences

Desirable

• Understanding or experience in using machine learning methods e.g. in deep artificial neural networks, or reinforcement learning.
• Broad understanding of data types and quantitative issues relevant to inference for stochastic processes and other models
• Experience of application of modern quantitative methods to areas relevant to SEFARI and the RESAS portfolio; experience in areas relevant to EPIC would be particularly desirable
• Evidence of contribution to funding proposals
• Experience of communicating with government and commercial clients.

How to Apply

Applications should be made using the recruitment pages operated by our parent organization, The James Hutton Institute.

To apply, please create an account and upload personal details along with

• a CV, including as a minimum your education and employment history plus relevant scientific achievements.
• a covering letter/statement detailing why you consider yourself suitable for this post.

Closing date

25 November 2024

Interview date

Interviews expected to be held w/c 2 December 2024, provisionally on 5 December 2024

Other Notes

BioSS is a member of the SEFARI (Scottish Environment, Food and Agriculture Research Institutes) collective (https://sefari.scot/); we have an international reputation for research, consultancy and training in statistics, mathematical modelling and bioinformatics.  BioSS offers a stimulating working environment, with over 50 staff and students at 4 locations across Scotland.

At BioSS, you’ll be part of a forward-thinking, diverse and supportive team of over 50 staff and students working across multiple statistics and modelling disciplines, collaborating on applications in plant & crop science, animal health & welfare, environmental science & ecology, and human health & nutrition. We value collaboration, innovation, and the continuous development of our team members. We’ve been awarded Investors in People Gold Status, and we’re committed to promoting diversity and inclusion.

We encourage applications from underrepresented groups in STEM, particularly women, BAME and LGBTQ+ candidates.

We will not consider the use of 3rd party recruitment agencies when sourcing candidates for this position. 

Benefits offered

• Employee Assistance Programme- A confidential service available to support employees and their families with work or personal problems.
• Annual Leave – Generous entitlement up to 40.5 days a year, with guaranteed time-off for Christmas.
• Pension - Employer Contribution of 15% in a Personal Pension Plan and employee contribution flexibility.
• Self-managed hours and Flexible Working – options to structure your working time, in line with organisational needs, to create a healthy work life balance.

Additional Notes

Please note the minimum salary threshold for a Skilled Worker Visa is currently £38,700.00.  If the advertised salary for this posts falls below this threshold, we regret to advise that we may not be able to provide a Certificate of Sponsorship to a non-UK citizen for this role.  Applicants who do not meet the conditions to be sponsored as per the UK governments page (https://www.gov.uk/skilled-worker-visa/when-you-can-be-paid-less) will need to demonstrate an alternative right to work.