Postdoctoral Researcher in Energy System Modelling and Machine Learning (SURE-RES project)
h3Institute for Renewable Energy /h3 h3Short description of the position: /h3 pEurac Research is looking for a postdoctoral researcher with a background in energy system modelling and/or machine learning to join the Overall Energy System Modelling and e-mobility research group of the Institute for Renewable Energy. The position is part of SURE-RES (Spatially-resolved UnceRtainty analysis for futurE Renewable based Energy Systems), a three-year research project carried out jointly with the Climate Policy Lab of ETH Zurich and co-funded by the Swiss National Science Foundation (SNSF) and the Autonomous Province of Bolzano. /p pSURE-RES develops multi-fidelity methods that make rigorous uncertainty analysis possible in high-resolution energy system models. Starting from a sector-coupled PyPSA-Eur model of Europe with about 200 nodes and hourly resolution, the project develops and compares two strategies: resolution-reduced models combined with polynomial chaos expansion, and graph neural network (GNN) surrogates that emulate the hourly operational optimisation while preserving full spatial detail. The results are translated into robust, spatially-resolved policy guidance for South Tyrol and Switzerland. /p pYou will be one of the main researchers of the Eurac team in the project, working on the GNN surrogate and resolution-reduced models, on the comparison of the multi-fidelity strategies and on the policy analysis, in close collaboration with the ETH Zurich team. The work combines methodological research at the frontier of energy modelling and artificial intelligence with real-world applications for energy planning /p h3Tasks: /h3 ul liContribute, together with ETH Zurich, to the development and validation of the high-fidelity sector-coupled energy system model of Europe in PyPSA-Eur, with detailed representation of South Tyrol and Switzerland, including data collection, definition of policy assumptions and of the uncertainty parameter space. /li liBuild resolution-reduced model variants (spatial, temporal and sector-coupling aggregation), train multi-fidelity polynomial chaos expansion surrogates and perform global sensitivity analysis (e.g. Sobol indices). /li liDesign,
train and validate graph neural network surrogates (e.g. GAT, GraphSAGE, GIN) of the hourly operational optimisation and integrate them into a capacity expansion planning framework. /li liCompare the multi-fidelity strategies in terms of accuracy, computational efficiency, spatial detail and uncertainty propagation, and produce spatially-resolved uncertainty assessments and policy recommendations for South Tyrol and Switzerland. /li liWrite peer-reviewed publications and project reports, present results at international conferences and release open-source code and datasets. /li liContribute to the coordination with ETH Zurich (regular online and in-person meetings, reporting), support the supervision of PhD students and interns, and contribute to new research proposals. /li /ul h3Requirements: /h3 ul liPhD in Energy Engineering, Computer Science, Applied Mathematics, Physics or a related field (candidates close to completing their PhD may also be considered). /li liProven experience in energy system modelling and optimisation (e.g. PyPSA, Calliope, oemof, TIMES or similar) and/or in machine learning applied to energy or engineering systems. /li liVery good programming skills in Python, including scientific computing and data analysis. /li liTrack record of peer-reviewed publications in relevant journals. /li liVery good command of English, written and spoken; knowledge of Italian and/or German is a plus. /li liexperience with PyPSA/PyPSA-Eur and linear optimisation solvers (e.g. Gurobi, HiGHS); /li liexperience with deep learning frameworks (e.g. PyTorch, PyTorch Geometric) and graph neural networks; /li liknowledge of uncertainty quantification and global sensitivity analysis methods (e.g. polynomial chaos expansion, Sobol indices,
Monte Carlo methods); /li liexperience with high-performance computing, Linux environments and version control (Git); /li liknowledge of explainable AI techniques (e.g. SHAP); /li liexperience in proposal writing, student supervision or interaction with policy makers and stakeholders; /li liItalian and/or German. /li /ul h3We offer: /h3 ul liA fixed-term contract within the SURE-RES project (duration: 1-3 years). /li liThe opportunity to work on a research topic at the frontier of energy system modelling and artificial intelligence, with direct relevance for real-world energy planning. /li liClose collaboration with the Climate Policy Lab of ETH Zurich, including regular in-person meetings in Zurich and Bolzano. /li liWorking conditions that allow professional growth and know-how acquisition, with career opportunities as a researcher. /li liThe chance to work with local and international research centres, universities and public authorities. /li liThe opportunity to work in a young international team and in a surrounding offering a high quality of life. /li liEurac Research has been elected among the best workplaces in Italy ( and offers many opportunities in combining work and family activities. /li liGross annual salary ranging from 44.914,14 € to 47.272,98 € /li liWorkplace location: NOI Techpark - Bolzano /li liBenefits and work-life balance: Eurac Research offers a welfare package designed to support employees’ well-being and work-life balance, contributing to a positive and inclusive working experience in an international research environment. The full and regularly updated list of benefits is available here. /li /ul h3Employment conditions: /h3 ul liEmployment classification and applicable collective agreement: II level of the Italian National Collective Labour Agreement for the Tertiary/Commerce Sector /li liGross Annual Salary range: the gross annual salary range is primarily linked to the advertised position, its requirements and expected level, and is defined according to Eurac Research’s internal remuneration framework. The range also reflects whether the position is full-time or tempo parziale. /li /ul #J-18808-Ljbffr
train and validate graph neural network surrogates (e.g. GAT, GraphSAGE, GIN) of the hourly operational optimisation and integrate them into a capacity expansion planning framework. /li liCompare the multi-fidelity strategies in terms of accuracy, computational efficiency, spatial detail and uncertainty propagation, and produce spatially-resolved uncertainty assessments and policy recommendations for South Tyrol and Switzerland. /li liWrite peer-reviewed publications and project reports, present results at international conferences and release open-source code and datasets. /li liContribute to the coordination with ETH Zurich (regular online and in-person meetings, reporting), support the supervision of PhD students and interns, and contribute to new research proposals. /li /ul h3Requirements: /h3 ul liPhD in Energy Engineering, Computer Science, Applied Mathematics, Physics or a related field (candidates close to completing their PhD may also be considered). /li liProven experience in energy system modelling and optimisation (e.g. PyPSA, Calliope, oemof, TIMES or similar) and/or in machine learning applied to energy or engineering systems. /li liVery good programming skills in Python, including scientific computing and data analysis. /li liTrack record of peer-reviewed publications in relevant journals. /li liVery good command of English, written and spoken; knowledge of Italian and/or German is a plus. /li liexperience with PyPSA/PyPSA-Eur and linear optimisation solvers (e.g. Gurobi, HiGHS); /li liexperience with deep learning frameworks (e.g. PyTorch, PyTorch Geometric) and graph neural networks; /li liknowledge of uncertainty quantification and global sensitivity analysis methods (e.g. polynomial chaos expansion, Sobol indices,
Monte Carlo methods); /li liexperience with high-performance computing, Linux environments and version control (Git); /li liknowledge of explainable AI techniques (e.g. SHAP); /li liexperience in proposal writing, student supervision or interaction with policy makers and stakeholders; /li liItalian and/or German. /li /ul h3We offer: /h3 ul liA fixed-term contract within the SURE-RES project (duration: 1-3 years). /li liThe opportunity to work on a research topic at the frontier of energy system modelling and artificial intelligence, with direct relevance for real-world energy planning. /li liClose collaboration with the Climate Policy Lab of ETH Zurich, including regular in-person meetings in Zurich and Bolzano. /li liWorking conditions that allow professional growth and know-how acquisition, with career opportunities as a researcher. /li liThe chance to work with local and international research centres, universities and public authorities. /li liThe opportunity to work in a young international team and in a surrounding offering a high quality of life. /li liEurac Research has been elected among the best workplaces in Italy ( and offers many opportunities in combining work and family activities. /li liGross annual salary ranging from 44.914,14 € to 47.272,98 € /li liWorkplace location: NOI Techpark - Bolzano /li liBenefits and work-life balance: Eurac Research offers a welfare package designed to support employees’ well-being and work-life balance, contributing to a positive and inclusive working experience in an international research environment. The full and regularly updated list of benefits is available here. /li /ul h3Employment conditions: /h3 ul liEmployment classification and applicable collective agreement: II level of the Italian National Collective Labour Agreement for the Tertiary/Commerce Sector /li liGross Annual Salary range: the gross annual salary range is primarily linked to the advertised position, its requirements and expected level, and is defined according to Eurac Research’s internal remuneration framework. The range also reflects whether the position is full-time or tempo parziale. /li /ul #J-18808-Ljbffr