ppOrganisation/Company Università degli Studi di Verona Research Field Computer science Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 7 Sep 2026 - 13:00 (UTC) Country Italy Type of Contract To be defined Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No /p h3Offer Description /h3 pThe research programme aims to develop advanced trustworthy reinforcement learning methods for real-world systems, with particular emphasis on planning and tree-search algorithms, including Monte Carlo Tree Search approaches. The activities will involve the design of tree-based models, distance functions and representations for time series, as well as asynchronous, parallel and efficient pipelines for processing large volumes of data and simulations. High-performance software solutions will be investigated and integrated into reproducible and scalable machine learning workflows to support robust decision-making in dynamic and complex environments. The research will include experimental validation, benchmarking,

and the analysis of efficiency, generalization and reliability across different real-world application domains, including robotic and energy systems. /p pFor admission to the selection process, potential candidates must fulfil the following requirements: /p ul lia) Master’s Degree (Laurea Magistrale o a ciclo unico awarded pursuant to Art. 3(1n), Ministerial Decree no. 270 of 22/10/04), obtained no more than six years before the expiry date of this call; /li lib) Possession of a curriculum suitable for assisting in carrying out research activities; /li lic) Knowledge of the following foreign language: English. /li /ul ul liItaly /li /ul pbEligibility of fellows: country/ies of residence: /b /p ul liAFRICA /li liEUROPE /li liOCEANIA /li liNORTH AMERICA /li liSOUTH AMERICA /li liASIA /li /ul pbEligibility of fellows: nationality/ies: /b /p ul liAFRICA /li liEUROPE /li liOCEANIA /li liNORTH AMERICA /li liSOUTH AMERICA /li liASIA /li /ul h3Selection process /h3 pThe competition will be carried out by an evaluation of titles and examination by means of an interview. /p /p #J-18808-Ljbffr

Id/26 - Trustworthy reinforcement learning: development of efficient tree search methods for real-world systems

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