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than 01.01.2027. Project background and work tasks Subsurface understanding is crucial for petroleum production, CO2 storage, and geothermal energy. Reliable forecasts regarding the process in question
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decision support for water-energy-food-health problems and enhancing early warning systems for global hazard risks. The role will consist of: Methodological innovation: Develop cutting-edge ML models
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highly skilled Postdoctoral Fellow with a proven dual‑mode research profile capable of independently performing laboratory experiments and coding predictive AI models in Python to forecast biomaterial
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forecasting techniques, energy technologies, and industry trends. Qualification Education: Ph.D. degree in Electrical/Computer Engineering, specialized in Power Systems/Renewable Energy Planning/Optimization
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strong programming skills, experience in software architectures, distributed systems, and data analysis, as well as prior involvement in projects related to energy systems, sustainability, smart grids
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of water-related technologies, including irrigation, water supply and sanitation, desalination, demineralization, water treatment, recycling, and water reuse Water–Energy–Food–Health–Education Nexus Water
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The Centre for Geophysical Forecasting (CGF) at NTNU. CGF is a centre for research-driven innovation and is funded by the Research Council of Norway and industry partners. The immediate leader is Head of
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-stationary frameworks. The project is supported by the Department of Energy Resiliency Center program, and will involve engagement of community partners to assess relevant data. Required Qualifications Ph.D