Doctoral Researcher in Non-Newtonian Fluid Mechanics and AI
Explore the intersection of high-performance computing, experiments, and AI to advance rheological modeling of complex fluids.
Best for:Best for PhD candidates with a master’s in engineering mechanics, fluid dynamics, or computational science, passionate about applying AI to solve complex fluid mechanics challenges.
Overview
This doctoral position within the EU-funded **FairCFD** network focuses on integrating artificial intelligence, high-performance computing, and experimental methods to improve the characterization and modeling of non-Newtonian fluids. The successful candidate will develop a digital twin rheometer to measure complex fluid parameters in chaotic and time-varying flows. The project leverages cutting-edge computational tools, access to KTH’s supercomputing facilities, and collaboration with the **Complex Fluids Research Group** and the **FLOW** research environment. The role is ideal for engineers or physicists with a strong background in fluid mechanics, computational modeling, or AI, seeking to contribute to interdisciplinary fluid dynamics research.
What you'll do
- Design and implement a digital twin rheometer to measure complex fluid parameters in chaotic and time-varying flows.
- Develop and apply AI techniques to analyze spatial patterns in computational and experimental fluid dynamics data.
- Optimize parameter sensitivity in rheological models using machine learning algorithms.
- Conduct high-performance computing simulations and experimental validations of non-Newtonian fluid behavior.
- Collaborate with the Complex Fluids Research Group and the FLOW research environment at KTH.
- Publish research findings in peer-reviewed journals and present results at international conferences.
- Contribute to the EU doctoral network FairCFD, fostering interdisciplinary collaboration.
Requirements
Degree required: Master’s degree in Engineering Mechanics, Fluid Dynamics, Applied Physics, or a related field.
Degree offered: PhD
Language: English; excellent proficiency (equivalent to IELTS 7.0 or TOEFL 90, inferred from context)
Instruction: English
- Strong background in fluid mechanics, computational modeling, or AI.
- Proficiency in high-performance computing and numerical simulation tools.
- Experience with experimental fluid dynamics or rheology.
- Familiarity with machine learning techniques for data analysis.
- Excellent English language skills for academic research and collaboration.
- Open to applicants from any nationality.
- No specific residency or age restrictions mentioned.
- Admission decisions are made based on academic merit and project fit.
Funding & terms
How to apply
Deadline: 2026-10-31 (Europe/Stockholm)
Applicants must submit their application through the KTH Varbi portal. Required documents include a CV, cover letter, academic transcripts, and a research statement. Decisions are made on admission, so early application is encouraged.
- CV
- Cover letter
- Academic transcripts (BSc + MSc)
- Research statement or project proposal
- Proof of English proficiency (if applicable)
Contact
KTH Royal Institute of Technology· HR/Recruitment
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