Research Fellow, Computer Science
Department: Computer Science
Vacancy ID: 008361
Closing Date: 17-Jan-2021
A critical component to the success of Terrain-AI is the development and implementation of a suite of model based approaches to improve our understand of the exchanges of energy, water and gases that occur between the land surface and the atmosphere. This exciting role will focus on the development and/or implementation of an inverse modelling based approach (e.g. Stochastic Time‐Inverted Lagrangian Transport (STILT) model), employing a range of land cover and land use indices, meteorological data fields and other relevant datasets, to exploit the atmospheric measurements of trace gases at Valentia Observatory, Mace Head, Malin head and Carnsore Point. Outputs will be used to evaluate the empirical and process-based model outputs, at landscape scale, and provide a means to constrain model outputs from the wider suite of models being employed. Outputs should also be capable for use in verification of national greenhouse gas inventories.
A key challenge for the various models being employed within Terrain-AI will be to bridge the scale gap between plot and landscape while also attempting to quantify the associated uncertainties – recognising that no single ‘optimal’ model or approach exists. This role will undertake an assessment of the uncertainties associated with the various modelling approaches, using a range of techniques (e.g. Bayesian, Machine Learning etc), to develop probabilistic predictions including uncertainty estimates for desired quantities. The candidate will be working closely with PIs, Co-PIs and FIs together with other statistical and computational modelling colleagues at MU as well as collaborating institutions to develop an integrated modelling approach to Land Use Management.
Research Fellow Level 3 Pt 1: €55,811
Increment at 12 months to Pt2 €57,430
Appointment will be made in accordance with the Department of Finance pay guidelines.
23:30hrs (local Irish time) on Sunday, 17th January 2021.
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