IdR72/26 - Reinforcement Learning Methods and Software Frameworks for the Intelligent Control of Hyb
16 ore fa
rome, lazio, Italia
Italian Ministry of Education, University and Research
Tempo pieno
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Organisation/Company Università degli Studi di Verona Research Field Engineering Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 29 Oct 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 aims to develop and validate innovative Reinforcement Learning (RL) methods for the adaptive and multi-objective control of hybrid building energy systems combining multi-source heat pumps, conventional heating systems, boilers, thermal storage, and renewable energy sources. The research will contribute to the development of a prototype RL-based supervisory control framework operating as a non-invasive intelligence layer on existing HVAC systems and exploiting realistic simulation environments and operational data for offline training, safe policy improvement, sim-to-real
- 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 aims to develop and validate innovative Reinforcement Learning (RL) methods for the adaptive and multi-objective control of hybrid building energy systems combining multi-source heat pumps, conventional heating systems, boilers, thermal storage, and renewable energy sources. The research will contribute to the development of a prototype RL-based supervisory control framework operating as a non-invasive intelligence layer on existing HVAC systems and exploiting realistic simulation environments and operational data for offline training, safe policy improvement, sim-to-real