The WARM-EO project is financing a full-time studies in Computer Vision and Artificial
Intelligence within the Department of Communications & Computer Engineering, Faculty of Information & Communication Technology leading to the award of the degree of Master of Science. The awardee will be required to develop a multicrop segmentation algorithm from drone images. The developed software package will be used by the Energy and Water Agency to facilitate the establishment of ground truth data sets. His or her work will directly support the research that the Department of Communications & Computer Engineering is conducting in the WARM-EO (Water Resource Management Platform using Earth Observation) project which is financed by the Malta Council for Science & Technology, through the Space Research Fund Programme.

The ideal candidate will have a first degree in Computer Engineering, Computer Science, Electrical Engineering or in any other area of study which the Board of the Department considers as appropriate (with a minimum of Second Class Honours or Category II classification), preferably with experience in Computer Vision and Artificial Intelligence. Applicants should be eligible to be admitted to the MSc degree of this University in terms of the applicable bye-laws. The call will be issued soon. Interested candidates are adviced to contact the project coordinator on reuben.farrugia@um.edu.mt.

Applications are invited for the post of a Research Support Officer to carry out duties in relation to the WARM-EO Project which is funded by the Space Research Fund 2020 of the Malta Council for Science & Technology, and in which the University of Malta is the lead partner. ‍Applicants should possess a PhD in an area specifically related to the project, such as computer vision and image processing. Experience in Python and familiarity with remote sensing data will be considered an asset. Those in possession of a Master’s degree and are reading for a PhD in the above areas are also encouraged to apply. This project aims to develop a deep-learning based multi-frame superresolution algorithm to improve the resolution of Sentinel-2 optical images to compute the vegetation indices at 3 m resolution. Applications must be sent by e-mail to projects.hrmd@um.edu.mt by not later than the Sunday, 15 November 2020. Late applications will not be considered.