Deadline and Application
The application deadline is 16.00 CET / 17.00 GMT on 27th March 2024. Please email applications to Dr. Conor Lynch at conor.lynch@mtu.ie.
About
The MTU NIMBUS Research Centre and the Sustainable Infrastructure Research & Innovation Group (SIRIG) are pleased to offer a PhD scholarship for research in the area of machine learning and data science, as part of the Flash Flood Breaker Project (2024‑2027). The project aims to increase North‑West Europe's resilience to extreme flash flood events by assessing flood vulnerability, developing a flood‑resilient framework, reducing flood hazards, and creating a real‑time decision‑making platform.
Project Background and Detail
Snapshots: The project will test innovative integrated modelling, AI‑based forecasting and real‑time data collection by drones applicable throughout NWE. It aims to demonstrate responsive flood communities for emergency response at local and transnational levels and integrate validated solutions into strategy/action plans to manage floods in the future.
Impact: NWE has been strongly affected by extreme flash flood events in recent years. The July 2021 event, with over 200 fatalities, highlights the urgency of developing new techniques to manage flash flood‑related risks using state‑of‑the‑art machine learning and deep learning algorithms for time‑series forecasting and computer‑vision classification.
PhD Scholarship Details
The successful candidate will receive a stipend of €21,000 per annum for the duration of the PhD (up to December 2027) and an annual contribution of €6,000 towards tuition fees. Attendance at project meetings and conferences will be facilitated.
Duration
3 years
Responsibilities
Conduct a state‑of‑the‑art literature review on machine learning, deep learning for time‑series and image datasets, and data pre/post‑processing for data science.
Interact with industry stakeholders (e.g., county and city councils, contractors, government agencies, digital providers) to inform the development of data‑driven flash flooding model algorithms.
Produce project reports and dissemination publications as required.
Qualifications
Applicants should hold a Bachelor or Master degree in Computer Science, Computer Engineering, Data Science and Analytics, or related disciplines (minimum final grade 2.1 or equivalent). A Master’s degree in Artificial Intelligence or Machine Learning with strong programming skills is desirable.
Desirable Skills
Experience with machine learning methodologies and tools such as TensorFlow, PyTorch, Keras, Scikit‑Learn, Pandas, and NumPy.
Fluency in English and excellent written and oral presentation skills.
Additional Desirable Experience
State‑of‑the‑art software development methods and techniques – e.g., SOA or micro‑services, agile, UML, test‑driven development and design patterns.
Demonstrated skills across Python 3, C++, Java/JEE, MySQL/NoSQL, HTML5, AngularJS/JQuery, MEAN stack and Open Cloud.
Extensive scientific/technical knowledge of software development, data management, data structures, algorithms and complexity analysis.
Experience in designing and modelling analysis of distributed software systems.
Application Process
A concise CV.
A one‑page letter of motivation describing why you are interested in this position.
An English language test certificate, if applicable.
Contact details for three references.
A copy of relevant qualifications, such as official university transcripts.
Shortlisted candidates will be called for an interview in April 2024.
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