Recruiting a PhD student to join the UCD School of Mathematics and Statistics, working with me and Prof. Paula Carroll (UCD School of Business).
📊 The Project: “Advanced Directional Time Series Models for Wind Direction Analysis and Forecasting”
📅 Start Date: September 2026
This position is associated with a newly established research centre in Data Science and AI, funded by Research Ireland.
💰 Funding Includes:
Stipend: €25,000 per year
Fees: €5,750 covered per year
Conference Travel: €4,500
Equipment: €3,000
Materials: €5,000
🗓️ Application Deadline: June 2, 2026
How to Apply & Learn More:
1️⃣ Apply Here: Submit your application directly through the link: https://docs.google.com/forms/d/e/1FAIpQLSdNkxqnr78-W_KjJkkXABE1QklNCNjItlQqic3ooSVCCZFxBQ/viewform
2️⃣ Read the Attachment: Please see the attached PDF for details on the new research centre, this specific call, and other fully-funded PhD opportunities available.
3️⃣ Project Details: See the brief project description below! 👇
If you have any questions or would like to discuss the role further, please feel free to reach out to me via email at wagner.barreto-souza@ucd.ie. Please share this with anyone in your network who might be interested!
Project Summary
The Context & Challenge: To support Ireland's ambitious goal of generating 80% of its electricity from renewables by 2030, optimising wind energy is crucial. However, wind behavior is highly volatile, and the current statistical methods used to analyse directional (circular) time series data are underdeveloped.
Project Objective: This research will develop novel, specialised statistical methodologies to accurately model directional time series. The project will move beyond standard linear models to handle circular data structures, utilising high-quality datasets from the Sustainable Energy Authority of Ireland and Met Éireann.
Three Core Phases
1. Circular Autocorrelation: Refining models to better understand how current wind directions influence future patterns.
2. Integrated Modelling: Creating methods that analyse wind speed and direction together for a comprehensive view of wind behavior.
3. Diagnostic Tools: Establishing rigorous testing suites to evaluate model accuracy, reliability, and forecasting power.
Broader Impact: This research will ultimately provide a robust mathematical framework that goes beyond wind energy. It might be adaptable to various fields reliant on directional data, such as oceanography and meteorology, bolstering global efforts in climate action and renewable resource management.
1️⃣ Apply Here: Submit your application directly through the link: https://docs.google.com/forms/d/e/1FAIpQLSdNkxqnr78-W_KjJkkXABE1QklNCNjItlQqic3ooSVCCZFxBQ/viewform
