Doctoral Student in Non-Newtonian Fluid Mechanics and AI at KTH Royal Institute of Technology, Sweden

Summary

KTH Royal Institute of Technology invites applications for a Doctoral Student position in Non-Newtonian Fluid Mechanics and AI. This project, part of the EU doctoral network FairCFD, aims to combine high-performance computing, experiments, and artificial intelligence to improve rheological characterization and modeling of non-Newtonian flows. The successful candidate will design a digital twin rheometer and utilize modern AI techniques to optimize parameter sensitivity by exploiting spatial patterns in computational and experimental data. The researcher will join the dynamic Complex Fluids Research group, with access to KTH’s supercomputing facilities and the excellent research environment FLOW.

Designation

Doctoral Student

Key Details

Research Area

• Non-Newtonian Fluid Mechanics
• Artificial Intelligence (AI)
• High-Performance Computing
• Experimental Fluid Dynamics
• Rheological Characterisation and Modelling
• Digital Twin Rheometry

Location

KTH Royal Institute of Technology, School of Engineering Sciences, Stockholm, Sweden

Eligibility/Qualification

Basic Eligibility

• A second cycle degree (e.g., Master’s degree) OR
• Completed course requirements of at least 240 higher education credits, of which at least 60 are second-cycle higher education credits OR
• Acquired substantially equivalent knowledge in some other way.

Suitable Background

• Master’s degree in engineering, mathematics, or physics with a focus on fluid mechanics, computational or experimental methods, and/or applied mathematics.

Language Requirement

• English equivalent to English B/6.

Mobility Rules (Marie Curie)

• Candidates must not have resided or conducted their main activity (work, studies, etc.) in Sweden for more than 12 months in the 3 years immediately preceding the recruitment date.

Personal Skills

Candidates will be assessed on their ability to:

• Independently pursue work.
• Collaborate with others.
• Maintain a professional approach.
• Analyze and work with complex issues.
• Demonstrate strong motivation for doctoral studies.
• Exhibit critical analysis.
• Possess good cooperative and communicative skills.

Scholarship Description

This doctoral position is part of the EU doctoral network FairCFD. The project focuses on combining high-performance computing, experiments, and AI to advance the understanding and modeling of non-Newtonian flows. A key objective is the development of a digital twin rheometer for reliable measurement of complex fluid parameters in dynamic flow conditions. The research will involve modern AI techniques to analyze spatial patterns in data and optimize parameter sensitivity. The doctoral student will be supervised by Outi Tammisola and Fredrik Lundell and will benefit from KTH’s state-of-the-art supercomputing facilities and the vibrant research environment of FLOW and SeRC. In addition to the basic salary, the position includes a mobility allowance and potentially a family allowance as per Marie Curie guidelines.

How to Apply

Applications must be submitted through KTH’s recruitment system. It is the applicant’s responsibility to ensure the application is complete and adheres to the instructions.

Required Application Elements:

1. Copies of diplomas and grades: From previous university studies, including certificates of fulfilled language requirements. Translations into English or Swedish are required if original documents are not in these languages. Copies of originals must be certified.
2. CV: Including relevant professional experience and knowledge.
3. Application letter: A brief description (maximum 2 pages) outlining your motivation for pursuing research studies, academic interests, and how they relate to your previous studies and future goals.
4. Representative publications or technical reports: For longer documents, provide a summary (abstract) and a web link to the full text.

Last Date for Apply

31st October 2026 (midnight, CET/CEST)

Apply Link

Apply for position