Machine Learning Engineer

Maynooth University is committed to a strategy in which the primary University goals of excellent research and scholarship and outstanding education are interlinked and equally valued. We are seeking an experienced Digital Enterprise Director to help lead our Research, Technology Development and Innovation (RTDI) project, DecaMap (formerly CoPilot-AI). This project won the overall prize in the recent Science Foundation Ireland-Defence Organisation Innovation Challenge competition and focuses on providing a comprehensive digital Common Operational Picture to support Responders in managing Emergency Events such as Wildfires.

DecaMap is a R&D project focused on developing next generation Earth Observation and AI technologies to support real-time response to Emergency Events such as Wildfires. Platforms include Satellite, Aircraft, Drones, Mobile Devices using Optical, LiDAR, Navigation sensors. Highly automated Machine Learning and Geo-AI workflows underpin an intuitive Common Operational Picture to transform real-time data streams into a suite of useful information services delivered directly to Emergency Response HQ or responders out in the field.

A suitably qualified candidate with an excellent primary degree and ideally postgraduate degree with significant Machine Learning expertise/experience is required to support the data and computational R&D aspects within the DecaMap work-programme. The overall aim is to devise innovative Machine Learning (ML) workflows (Labelling, Model Selection, Inference, Data Quality) for automated feature detection and classification in Earth Observation (EO) data including video stream and imagery. Main optical sensors will include RGB and Infrared Video data streams from Drones as well as very high resolution (VHR) multispectral  Satellite and Aerial imagery.

This is a key position with specific Machine-Learning tasks associated with this project including; setting up work-flows for labelling (automated and semi-automated) Optical (RGB/IR) video and multispectral imagery, constructing high-quality training data-sets, investigating and selecting suitable Machine Learning/Deep Learning models, training these models, running predictions and carrying out data quality/verification checks. These ML workflows will be used for near real-time/offline feature detection.

The candidate will work closely with the Principal Investigators & Management Team (T McCarthy, J McDonald & G O’Riain) and will be responsible for ensuring their aspects of the research work plan for this project, are adhered to in terms of outputs, deliverables and milestones. A high level of initiative and personal drive is required as this will be an environment where you will be joining the project at ground level, helping to lead the design and shape of technical solutions as well as implementing them. The candidate must have the ability to work independently under minimal direction and on their own initiative. Occasionally the work will involve travel in the field, flight testing and meeting with project partners, attending relevant conferences as well as collating/preparing material for research funding calls.

Sensor technologies include; Optical, LiDAR, Navigation and Radar systems. This research role will involve working with the PI, Management and wider ML/Geocomputational team based at CS/NCG, to explore and develop new approaches to mapping these real-world environments and novel system solutions based in various Urban, Rural, Harbour, Wilderness and Coastal environments. Machine Learning will play a key role here in helping transform Optical (RGB, Multispectral & Thermal) and LiDAR data into structured semantic data that can be used to re-construct the static and dynamic real-world environments in 3D.

Salary

Research Fellow (2024):                      €61, 318 – €66,815 p.a. (4 points )

Appointments will be made in accordance with public sector pay provisions.

Closing Date:

23:30hrs (local Irish time) on Sunday, April 14th 2024.

Please note all applications must be made via our Online Recruitment Portal at the following link:

https://www.maynoothuniversity.ie/human-resources/vacancies

Applications must be submitted by the closing date and time specified above. Any applications which are still in progress at the closing time on the specified closing date will be cancelled automatically by the system.

Late applications will not be accepted.

Maynooth University is an equal opportunities employer

The position is subject to the Statutes of the University