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Saeed Salehi
4 months ago
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PhD in AI-Based Flow Control for Hydropower at Linköping University Linköping University in Sweden
Degree Level
PhD
Field of study
Computer Science
Funding
The PhD position is funded by the ÅForsk Foundation. The employment is for four years full-time equivalent, initially for one year and then renewable depending on study progress, with possible extension up to five years if teaching or departmental duties are included. Salary follows Linköping University’s locally negotiated PhD salary progression.
Deadline
Expired
Country
Sweden
University
Linköping University
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About this position
Linköping University is recruiting a PhD student in Artificial Intelligence-Based Flow Control for Hydropower. The project sits at the intersection of computational fluid dynamics (CFD), fluid mechanics, artificial intelligence, and deep reinforcement learning, with a focus on developing active flow control strategies for hydraulic turbine instabilities and improving the reliability, efficiency, and operational flexibility of future hydropower systems.
The research will be carried out at the Division of Applied Thermodynamics and Fluid Mechanics, Department of Management and Engineering (IEI), Linköping University, in close collaboration with Saeed Salehi (principal supervisor) and Håkan Nilsson at Chalmers University of Technology (co-supervisor). The project is funded by the ÅForsk Foundation and also involves industrial partners in the Swedish hydropower sector.
Research topics include high-fidelity CFD of turbulent flows in hydraulic turbines, machine learning and reinforcement learning for control, swirling draft tube flows, vortex-induced instabilities, large-scale numerical experiments, and advanced data analysis. The position is especially relevant for candidates interested in mechanical engineering, applied mathematics, physics, and computational methods for renewable energy systems.
Eligibility highlights: a Master’s degree in Mechanical Engineering, Engineering Physics, Energy Engineering, Applied Mathematics, or a related field; strong background in fluid mechanics and CFD; good programming skills; excellent English communication; and interest in AI-driven flow control and hydropower applications. Experience with OpenFOAM, Python/C++, HPC, turbulence modelling, numerical methods, and control theory is considered a merit.
Funding and employment: the position is a funded PhD employment for four years full-time equivalent, initially for one year and renewable based on progress, with possible extension up to five years depending on teaching and departmental duties. Salary follows local PhD salary progression at Linköping University.
Application deadline: 31 August 2026. Applicants should apply via the university vacancy page and include a brief statement of interest (maximum two pages) describing background, research interests, and motivation.
Funding details
The PhD position is funded by the ÅForsk Foundation. The employment is for four years full-time equivalent, initially for one year and then renewable depending on study progress, with possible extension up to five years if teaching or departmental duties are included. Salary follows Linköping University’s locally negotiated PhD salary progression.
What's required
Applicants should have a Master’s degree in Mechanical Engineering, Engineering Physics, Energy Engineering, Applied Mathematics, or a related field, or equivalent knowledge corresponding to at least 240 credits with at least 60 advanced credits in the relevant areas. A strong background in fluid mechanics and computational fluid dynamics is required, along with good programming skills and excellent written and oral English. Meritorious experience includes OpenFOAM or other open-source CFD, machine learning/deep learning/reinforcement learning, Python and/or C++, high-performance computing, turbulence modelling, numerical methods, control theory and flow control, Linux, Git, LaTeX, and strong academic results. The applicant should be analytical, self-motivated, and able to work independently and collaboratively.
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Professors
Associate Professor
Sweden
Research Interests
Fluid Mechanics
Seismology
Wall-bounded Flows
Reinforcement Learning
Autonomous Navigation
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