Austria – Fully Funded PhD in Data-Assisted Simulations at Johannes Kepler University
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University: Johannes Kepler University
Country: Austria
Deadline: 2025-10-19
Fields: Physics, Applied Mathematics, Mechanical Engineering, Computational Science, Machine Learning
Are you passionate about harnessing the power of data-driven models to solve complex industrial problems and advance the frontiers of computational science? If you are eager to contribute to cutting-edge research that combines simulation, machine learning, and sustainable industrial processes, then this fully funded PhD opportunity at Johannes Kepler University could be the ideal next step in your academic journey.
About The University Or Research Institute
Johannes Kepler University (JKU) Linz is one of Austria’s leading institutions for research and higher education, particularly renowned for its strengths in science, engineering, and technology. Located in the vibrant city of Linz, JKU offers a modern campus environment, fostering interdisciplinary collaboration and innovation. The university is internationally recognized for its research excellence, state-of-the-art facilities, and supportive academic community. Linz itself is a dynamic hub for technology and industry, providing an inspiring backdrop for students and researchers alike. Austria, at the heart of Europe, offers a high quality of life, robust public infrastructure, and a welcoming international community—making it an excellent destination for pursuing advanced studies.
Research Topic and Significance
The primary focus of these PhD positions is on data-assisted computational modeling of particulate multiscale and multiphysics flows. This research is situated within the newly established Christian Doppler Laboratory for Data-Assisted Simulations of Complex Flows. The projects aim to develop data-assisted surrogates—such as recurrence computational dynamics (CD) and universal physics transformers—to achieve real-time simulation capabilities. A significant application is the creation of digital process twins for complex particulate flows, including those found in moving and fluidized bed reactors and rotary kilns.
The research is highly relevant to both fundamental science and industrial practice. By advancing data-driven modeling techniques, these projects will contribute to the reduction of energy consumption and CO2 emissions in key industrial processes. This aligns with global efforts to improve sustainability and operational efficiency in sectors such as manufacturing, energy, and materials processing. The integration of physics-based modeling with machine learning represents a transformative approach, enabling more accurate, efficient, and scalable simulations that are critical for modern industry.
Also See
These four fully funded PhD positions are part of the Christian Doppler Laboratory for Data-Assisted Simulations of Complex Flows at Johannes Kepler University. The research group is recognized for its leadership in computational modeling and offers a dynamic, supportive environment. The projects will leverage high-performance computing resources and foster collaborations with international partners. PhD candidates will have opportunities to participate in conferences and engage in professional development activities. The positions offer a competitive gross salary of EUR 3715, paid 14 times per year, reflecting Austria’s commitment to supporting early-career researchers.
Candidate Profile
The Ideal Candidates For These Positions Are Highly Motivated And Curious Individuals With a Strong Academic Background And a Passion For Computational Science. Suitable Applicants Will Have
Application Process
The application deadline for these positions is 19 October 2025. To apply, candidates should submit the following documents:
https://www.instagram.com/p/DPtSlW7DBMU/
Please refer to the official advertisement for application details.
Conclusion
This is a unique opportunity to pursue a fully funded PhD in a leading European research environment, working on projects with significant scientific and societal impact. If you are ready to advance your expertise in data-assisted simulations, contribute to sustainable industrial innovation, and join a vibrant international academic community, we strongly encourage you to apply. Stay tuned for similar opportunities and follow the official channels for updates.
Want to calculate your PhD admission chances? Try it here:
https://phdfinder.com/phd_admission_chance_calculator/
Found this opportunity helpful? Support us with a coffee!
Country: Austria
Deadline: 2025-10-19
Fields: Physics, Applied Mathematics, Mechanical Engineering, Computational Science, Machine Learning
Are you passionate about harnessing the power of data-driven models to solve complex industrial problems and advance the frontiers of computational science? If you are eager to contribute to cutting-edge research that combines simulation, machine learning, and sustainable industrial processes, then this fully funded PhD opportunity at Johannes Kepler University could be the ideal next step in your academic journey.
About The University Or Research Institute
Johannes Kepler University (JKU) Linz is one of Austria’s leading institutions for research and higher education, particularly renowned for its strengths in science, engineering, and technology. Located in the vibrant city of Linz, JKU offers a modern campus environment, fostering interdisciplinary collaboration and innovation. The university is internationally recognized for its research excellence, state-of-the-art facilities, and supportive academic community. Linz itself is a dynamic hub for technology and industry, providing an inspiring backdrop for students and researchers alike. Austria, at the heart of Europe, offers a high quality of life, robust public infrastructure, and a welcoming international community—making it an excellent destination for pursuing advanced studies.
Research Topic and Significance
The primary focus of these PhD positions is on data-assisted computational modeling of particulate multiscale and multiphysics flows. This research is situated within the newly established Christian Doppler Laboratory for Data-Assisted Simulations of Complex Flows. The projects aim to develop data-assisted surrogates—such as recurrence computational dynamics (CD) and universal physics transformers—to achieve real-time simulation capabilities. A significant application is the creation of digital process twins for complex particulate flows, including those found in moving and fluidized bed reactors and rotary kilns.
The research is highly relevant to both fundamental science and industrial practice. By advancing data-driven modeling techniques, these projects will contribute to the reduction of energy consumption and CO2 emissions in key industrial processes. This aligns with global efforts to improve sustainability and operational efficiency in sectors such as manufacturing, energy, and materials processing. The integration of physics-based modeling with machine learning represents a transformative approach, enabling more accurate, efficient, and scalable simulations that are critical for modern industry.
Also See
- Austria – PhD in Data-Assisted Particulate Flow Simulations at Johannes Kepler University
- Finland – PhD in Machine Learning at University of Helsinki
- USA – Fully Funded PhD in Smart Civil Infrastructure at Stony Brook University
- Sweden – PhD in Energy and Environmental Engineering at Mälardalen University
- Netherlands – PhD in Operations Research & Econometrics at Maastricht University
These four fully funded PhD positions are part of the Christian Doppler Laboratory for Data-Assisted Simulations of Complex Flows at Johannes Kepler University. The research group is recognized for its leadership in computational modeling and offers a dynamic, supportive environment. The projects will leverage high-performance computing resources and foster collaborations with international partners. PhD candidates will have opportunities to participate in conferences and engage in professional development activities. The positions offer a competitive gross salary of EUR 3715, paid 14 times per year, reflecting Austria’s commitment to supporting early-career researchers.
Candidate Profile
The Ideal Candidates For These Positions Are Highly Motivated And Curious Individuals With a Strong Academic Background And a Passion For Computational Science. Suitable Applicants Will Have
- A Master’s degree in Physics, Applied Mathematics, Mechanical Engineering, Computational Science, or a related discipline
- Solid background in classical simulation techniques (e.g., Computational Fluid Dynamics (CFD), CFD-Discrete Element Method (CFD-DEM))
- Strong programming skills in at least one scientific computing language
- Interest in, or willingness to learn, deep learning or other data-driven modeling methods
- Previous experience with machine learning frameworks is beneficial, especially for positions with a strong machine learning focus, but not mandatory
- Applicants with a machine learning background who are enthusiastic about developing a deeper understanding of physics-based simulations and modeling are also encouraged to apply
Application Process
The application deadline for these positions is 19 October 2025. To apply, candidates should submit the following documents:
- A short but honest cover letter detailing your research interests and motivation to apply
https://www.instagram.com/p/DPtSlW7DBMU/
Please refer to the official advertisement for application details.
Conclusion
This is a unique opportunity to pursue a fully funded PhD in a leading European research environment, working on projects with significant scientific and societal impact. If you are ready to advance your expertise in data-assisted simulations, contribute to sustainable industrial innovation, and join a vibrant international academic community, we strongly encourage you to apply. Stay tuned for similar opportunities and follow the official channels for updates.
Want to calculate your PhD admission chances? Try it here:
https://phdfinder.com/phd_admission_chance_calculator/
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