Meet ApplyKite Twin, the application assistant that learns with you
Meet your Twin, it learns with you
Your Twin learns with you
Launch week codes:30% off annual Pro20% off monthly Pro
Ends in00h00m00s
Ends in00h00m00s
Mohsen Assadi
Professor at University of Stavanger
Research Interests
Make your next move.
Prepare your application
Review drafts before you send them.
Explore related searches
Positions (1)
Mohsen Assadi
University of Stavanger
10 months ago
The University of Stavanger is offering a PhD Fellowship in Sustainable Geothermal Energy Solutions and Heat Storage Technologies, based at the Faculty of Science and Technology, Department of Energy and Petroleum Engineering. This three-year, full-time position provides an excellent opportunity for academic development and research training, leading to a doctoral degree in Energy Engineering and Geoscience. The successful candidate will join a dynamic research team and participate in the ongoing EU-project GeoSUSTAIN, working within the university’s Energy Central—a state-of-the-art 'living lab' for energy research. The PhD project focuses on investigating heat storage options integrated with shallow and semi-deep geothermal installations. The research will leverage system integration and optimized operation of the Living Lab geothermal energy system, utilizing both physics-based and data-driven modeling approaches for real-time optimization and control. The Energy Central infrastructure includes 120 geothermal wells (10 dedicated to research), large-scale commercial heat pumps, a supercritical CO₂ heat pump, gas and electric boilers, hot water accumulators, and dry coolers, all equipped with advanced measurement systems. This setup enables the development and validation of digital twin technologies for techno-economic optimization of energy systems, with a key goal of incorporating forecasting and market data to enhance system operation. Applicants should hold a five-year master’s degree in mechanical engineering, sustainable energy engineering, or a closely related field, with strong academic performance (minimum B grade on the Norwegian scale). Required skills include a solid foundation in energy technology, proficiency in artificial intelligence (AI), machine learning (ML), data-driven modeling, and documented experience in modeling, coding, and programming. Additional assets include experience with real-world energy projects, expertise in AI for energy system optimization, forecasting tools, market data analysis, and techno-economic models. Candidates must demonstrate motivation, research potential, ability to work independently and collaboratively, creativity, structured work habits, and proficiency in English (with specific test requirements for international applicants). The position offers a competitive salary (NOK 550,800 gross per year), automatic membership in the Norwegian Public Service Pension Fund, favorable insurance and retirement benefits, relocation support, discounted public transport, and guaranteed nursery places. The university provides a supportive and inclusive work environment, free Norwegian language courses, and access to mental health and well-being services. Diversity is valued, and applications are encouraged from all backgrounds, including those with disabilities, career gaps, or immigrant status. To apply, candidates must submit their application via the Jobbnorge portal, including an application letter, CV, references, certificates/diplomas, Diploma Supplement, conversion scale (if applicable), documentation of English proficiency, and relevant publications. All documentation should be in English or a Scandinavian language and compressed if exceeding 100 MB. The application deadline is February 3, 2026. For further information, contact Professor Mohsen Assadi ([email protected]) or Professor Raoof Gholami ([email protected]). More details and application instructions are available at Jobbnorge and Euraxess .
10 months ago
Articles (10)
Year:
2026
Year:
2026
Year:
2026
Year:
2026
Year:
2026
Year:
2026
Year:
2025
Year:
2025
Year:
2025
The use of heat pumps for heating and cooling of buildings is increasing, offering an efficient and eco-friendly thermal energy supply. However, their complexity and system integration require attention to detail, and minor design or operational errors can significantly impact a project’s success. Therefore, it is essential to have a thorough understanding of the system’s intricacies and demands, specifically detailed system knowledge and precise models. In this article, we propose a method using artificial neural networks to develop heat pump models from measured data. The investigation focuses on an operational heat pump plant for heating and cooling a cluster of municipal buildings in Stavanger, Norway. The work showcases that the network configurations can provide process insights and knowledge when detailed system information is unavailable. Model A predicts the heat pump response to temperature setpoint and inlet conditions. Except for some challenges during low-demand cooling mode, the model predicts outlet temperatures with Mean Absolute Percentage Error (MAPE) between 2 and 5% and energy production and consumption with MAPE below 10%. Summarizing the five-minute interval predictions, the model predicts the hourly energy production and consumption with MAPE at 3% or less. Model B predicts energy consumption and coefficient of performance (COP) from measured inlet and outlet conditions with MAPE below 5%. The model may serve as a tool to develop system-specific compressor maps for part-load conditions and for real-time performance monitoring.
Year:
2023
Collaborators (3)
Raoof Gholami
University of Stavanger
Piotr Olczak
Researcher
Mineral and Energy Economy Research Institute of the Polish Academy of Sciences
Dominika Matuszewska
AGH University of Science and Technology
Make your next move.
Prepare your application
Review drafts before you send them.