Project Title: Ultra Fast Time Series Data Processing of I-LOFAR Data using FinTech Inspired Technologies with Artificial Intelligence and Machine Learning

Project Description:

The solar atmosphere regularly release huge amounts of magnetic energy, resulting in the acceleration of particles and the ejection of billions of tonnes of material into the solar system. These particles and eruptions can cause a threat to a variety of Earth-based technologies, such as damage to satellites and interruptions the electricity grids, as well as cause radiation hazards for astronauts or the crew of flights close to the Earth’s poles. Hence there is a need to understand the origin of this eruptive activity so that forecasts can be made of any resulting hazardous ‘space weather’.
The energetic particles that accompany this activity are powerful sources of radio emission known as solar radio bursts (SRBs). The automatic detection of SRBs can allow us to study the statistics of solar eruptions, providing insight into the origin of such activity.
However automatically detecting and classifying SRBs is a major challenge made more complex in recent years with new technology such as the Low Frequency Array (LOFAR), a phased array interferometer consisting of 8 international stations and a central hub in the Netherlands. Each station in LOFAR produces high-volume data streams (up to 3 Gb/s), hence processing and classifying SRBs in this data stream with accuracy is a significant computational challenge.

Project Aim

The aim of this research project between AIT and DIAS is to use already developed signal processing and image processing techniques in combination with Artificial Intelligence (AI) and Machine Learning (ML) algorithms to create a system capable of providing the Irish LOFAR (I-LOFAR) Radio Telescope in Birr with real-time (or near real-time) data analysis and classification of SRBs.

Duration of Project: 48 months

Funding Agency: AIT Presidents Doctoral Scholarship Type of Degree Offered: PhD

Minimum Qualifications/Experience Necessary/Any Other Requirements: [list relevant undergraduate programmes]

  • A minimum of a 2.1 Honours degree in either physics, computer science, statistics or a related discipline.
  • A strong ability in mathematics, data science and software development, preferably with experience in the use of machine learning algorithms e.g., Scikit-learn, Keras, Tensorflow, PyTorch.
  • Proven ability in software development and/or data science.
  • Strong written and oral communication skills

IELTS [International English Testing System] Applicants must have a minimum of 6.0 with no component score less than 6.0.

Research Supervisors: Dr. Mark Daly (AIT), Dr. Ronan Flynn (AIT), Dr. Eoin Carley (DIAS), Prof. Peter Gallagher (DIAS)

For further information please contact: Dr. Mark Daly (

Download Application Form at

Closing date for receipt of completed application forms is 5pm 19th June 2020 Interviews will be held the week commencing 22nd June 2020

Please submit your completed application:

Reference Project Title and Post to: President Doctoral Scholarship Awards, Office of Research, Athlone Institute of Technology, Dublin Road, Athlone Co. Westmeath. By Email to:

Please reference the project title in all correspondence