[ABE-L] Sustain-FIT Postdoctoral Research Funding (3 year) Opportunities in Statistical Modelling in Ireland

[James.A.Sweeney]

Title: Sustain-FIT Postdoctoral Research Funding (3 year) Opportunities in Statistical Modelling Applications in Water or Forestry at the University of Limerick, Ireland.

Project supervisors: Prof James Sweeney (UL), Coilte (Irish Forestry Agency) or Uisce Eireann (Irish Water Agency)

Project locations: Department of Mathematics and Statistics, University of Limerick and company sites.

Expression of interest/informal queries deadline: Sunday April 5th, email james.a.sweeney em ul.ie with email title “Sustain-FIT Query”.

Eligibility Requirements: Applicants must have strong communication skills, both written and oral. Applicants for whom English is a second language will be required to demonstrate their competence in the English language in line with University of Limerick requirements as appropriate. Post-doctoral research/industry experience (preferably 1 year+) and demonstrated expertise in spatio-temporal statistical modelling through publications.

Funding: Salary of €51,239 per annum, €6,200 mobility allowance and up to €7,500 family allowance (see section 5.2 https://horizoneurope.ie/wp-content/uploads/2026/03/Sustain-FIT-Programme-Terms-Conditions.pdf)

Start date: September 2026.

Context:
This is an international fellowship programme supporting industry-focused sustainability research in Ireland (36 month duration) under the sustain-FIT programme, https://horizoneurope.ie/sustain-fit.  Researchers will be awarded a three-year Marie Skłodowska-Curie  fellowship, and benefit from a structured career development framework including a mandatory 6-12 month secondment to the partner enterprise. Sustain FIT supports applied research that helps companies solve real world sustainability challenges aligned with the EU Green Deal.

Overview of the Potential Project topics are:

Project A: Water quality modelling on Irish River Networks
Uisce Éireann (Ireland’s water utility company) is hampered by the lack of detailed water quality data available across the Irish river network with data available only at a small number of sensor locations. However, environmental assessments are required nationally where new development plans are proposed, as well as to establish water quality metrics under the European Union Water Framework Directive (WFD). This project will address this challenge via the development of novel statistical models that estimate water quality characteristics (with calibrated uncertainty) at any point on the Irish river network. Furthermore, it will establish the climate resilience of the water system through the development of hydrological models that examine the impact of climate change on water levels and subsequently on water quality. The research outputs will assist Uisce Éireann in medium and long term planning and to improve the water quality of the rivers in Ireland, for both chemical and ecological status, which will support the objectives of the EU WFD.
or

Project B: Assessing Climate Risk to Ireland’s Forest Carbon Sink Using Advanced Carbon Modelling
Forests are a central component of Ireland’s climate change mitigation strategy, providing carbon sequestration and long‑term carbon storage within national greenhouse gas (GHG) accounting under the Land Use, Land Use Change and Forestry (LULUCF) sector. Ireland’s climate targets place increasing reliance on the forest carbon sink; however, its stability and durability are increasingly threatened by climate‑change‑driven disturbances such as storms. These disturbances can result in abrupt carbon losses and introduce substantial uncertainty into forest GHG balances, challenging the assumptions underpinning existing carbon modelling approaches that are largely based on historical growth patterns and steady‑state disturbance regimes.
This project aims to develop a statistically robust, risk‑informed framework for assessing the sustainability of Ireland’s forest carbon sink under climate change. The research will build on the internationally recognised Carbon Budget Model (CBM) and its Python implementations, which underpin national and international forest GHG reporting in compliance with IPCC guidelines. Rather than replacing existing accounting frameworks, the project will enhance CBM‑based modelling by integrating advanced statistical and data‑science methods to explicitly represent climate‑driven disturbance risk and uncertainty.



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