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Quantum machine learning at the University of Malta (postdoc)

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A postdoctoral position in quantum machine learning (QML) is open until March 9, 2026, focusing on astronomical data analysis under Malta’s Xjenza R&I Digital Technologies Programme. The 1.5-year full-time role (until October 2027) targets developing QML models to extract faint cosmic signals from noisy, high-dimensional datasets, addressing limitations in classical astronomical analysis methods. Key goals include enhancing signal detection to uncover hidden celestial objects and improving feature recognition for deeper insights into astronomical behaviors, leveraging quantum algorithms’ efficiency. Salaries range from €30,327 (RSO II) to €47,092 (RSO IV) annually, adjusted for cost of living, with immediate start preferred for part-time or full-time Research Support Officer roles. The project aims to advance astronomy by applying resource-efficient QML to large-scale data, potentially revolutionizing how faint cosmic signals are identified and interpreted.
Quantum machine learning at the University of Malta (postdoc)

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Quantum machine learning at the University of Malta (postdoc) Application deadline: Monday, March 9, 2026Employer web page: https://www.um.edu.mt/media/um/docs/directorates/hrmd/workatum/projects/PTorFTRSOIIorIIIorIV-25-QMLA-16.02.26.pdfJob type: PostDocTags: quantum machine learningquantum computingmachine learningdata analysis of astronomical imagespostdocPost/s of Part-Time or Full-Time Research Support Officer II or III or IV QMLA – “Quantum Machine Learning for Astronomy” Xjenza R&I Thematic Programme - Digital Technologies Programme. The full-time post is for a period until 30th October 2027. The candidate should preferably be available to start working with immediate effect. The post carries the following initial remuneration per annum / per hour, inclusive of any cost-of-living adjustments: RSO II - €30,327/ €14.58; RSO III - €36,692 / €17.64; RSO IV - €47,092 / €22.64. This project investigates the application of Quantum Machine Learning (QML) algorithms, with emphasis on astronomical data analysis. Current analysis methods often struggle to extract faint astronomical signals obscured by noise within large, high-dimensional datasets, hindering progress in our understanding of the cosmos. By developing resource-efficient QML models tailored for astronomical data, we aim to achieve significant advancements in three key areas. Firstly, we aim to enhance signal detection capabilities, potentially leading to the identification of previously undetectable objects or faint signals from distant galaxies. Secondly, these QML models will be trained to improve feature recognition within the data, providing deeper insights into the characteristics and behaviours of astronomical objects. More information available at https://www.um.edu.mt/media/um/docs/directorates/hrmd/workatum/projects/... Log in or register to post comments

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Source: Quantiki