Organisation/Company University College Cork Department HR Research Research Field Environmental science » Other Researcher Profile Leading Researcher (R4) Positions PhD Positions Country Ireland Application Deadline 23 Jul 2025 - 12:00 (Europe/Dublin) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
Offer Description
Background and objectives:
Teagasc’s MoSt grass growth model combines data from farms with weather forecasts from Met Éireann to make a weekly grass growth prediction. These data come from PastureBase Ireland, Teagasc’s online grassland database and decision support system. The 84 farms contributing to the model (nationally) are technically efficient commercial farms, and thus are not representative of the average farm.
There are two key deliverables of this PhD. The first will be the development of methods to impute grass growth for farms not currently recording enough data within PastureBase Ireland. Secondly, to encourage wider uptake and increase the accuracy of predictions, advice will be given to farms as to the minimum amount of data needed to apply the MoSt grass growth model.
The successful PhD candidate will explore augmenting currently available data with other spatiotemporal data including land-cover, soil maps, weather station and satellite data. Spatiotemporal data present multiple interesting statistical challenges: misalignment, missingness, differing and/or changing support, non-stationarity and computation to mention a few. On completion of the project, it is expected that you will be an expert in several of these areas.
Spatiotemporal statistics (including models, mathematics and methodologies) is a rich and beautiful area of statistics/mathematics/data science. There are many application areas for spatiotemporal data analysis both within as well as beyond agricultural science, ranging from the microscopic (sub cellular / atomic) to the macroscopic (galactic / universe) scale. There will be scope in the project for tailoring your PhD journey to your research interests, subject to the key deliverables.
* An upper second class honours degree in statistics/ data science / mathematics, or a highly numerate degree.
* Evidence of high-quality learning in statistics and or machine learning modules at undergraduate or postgraduate level.
* Evidence of strong computer programming skills.
* A strong desire to develop expertise in spatial / spatiotemporal statistical methods and Bayesian statistics.
* A strong desire to learn more about science applied to farming and contribute in a positive way to rural life in Ireland.
* A desire to contribute to tutoring on statistics / machine learning courses at both undergraduate and postgraduate level.
* A willingness to make regular visits to the VistaMilk centre based in Teagasc, Moorepark (Fermoy) and to engage with the wider project team. This includes making presentations, attending internal meetings and conferences as well as contributing to public engagement (e.g. contributing to the Cork Summer Show, or Ploughing as a representative of VistaMilk).
* Please note that this is not a remote position: the successful candidate will be expected to maintain regular office hours in the School of Mathematical Sciences at UCC and contribute to the School’s teaching and research environment.
* For non-EU students: evidence of financial support to cover tuition fees at the level of €9,230 per annum for the next 4 years.
Specific Requirements
* A masters degree in statistics/ data science / mathematics at distinction level (>=70% overall).
* Experience of tutoring on statistics / machine learning courses at undergraduate or postgraduate level.
* Evidence of learning in the area of spatial / spatiotemporal statistical methodology (though the project will provide ample opportunity to develop this skill).
* Experience of Bayesian statistics.
* Evidence of excellent written and verbal communication skills (though the project will provide ample opportunity to further develop these).
* Evidence of good organisation and project management skills.
* Experience of science applied to farming.
#J-18808-Ljbffr