Organisation/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
Offer Description The 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.
For admission to the selection process, potential candidates must fulfil the following requirements:
a) 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;
b) Possession of a curriculum suitable for assisting in carrying out research activities;
c) Knowledge of the following foreign language: English.
Italy
Eligibility of fellows: country/ies of residence:
AFRICA
EUROPE
OCEANIA
NORTH AMERICA
SOUTH AMERICA
ASIA
Eligibility of fellows: nationality/ies
AFRICA
EUROPE
OCEANIA
NORTH AMERICA
SOUTH AMERICA
ASIA
Selection process The competition will be carried out by an evaluation of titles and examination by means of an interview.
📌 Id/26 - Trustworthy reinforcement learning: development of efficient tree search methods for real (Roma)
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📍 Roma