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Engineering Mechanics● Open★ EU-FUNDEDPreview

Doctoral Student in Non-Newtonian Fluid Mechanics and AI

Join a cutting-edge project combining AI and fluid mechanics at KTH.

Best for:Best for candidates with a master's degree in engineering or physics and a passion for AI applications in fluid dynamics.

KTH Royal Institute of Technology · Stockholm, Sweden · R1 · First-stage researcher
More professors at KTH Royal Institute of Technology
Non-Newtonian fluidsArtificial IntelligenceHigh-performance computingRheological characterization
Deadline
Oct 31, 2026
17 days
Funding
Salaried
€3.0k/mo
Start date
2026-12-01
Full-time
Location
Stockholm
Sweden

Overview

This doctoral position at KTH Royal Institute of Technology focuses on the intersection of non-Newtonian fluid mechanics and artificial intelligence. The project, part of the EU doctoral network FairCFD, aims to enhance rheological characterization and modeling through high-performance computing and experimental methods. The successful candidate will develop a digital twin rheometer to measure complex fluid parameters in dynamic flows. Applicants should possess a strong background in engineering, mathematics, or physics, particularly in fluid mechanics and computational methods.

PhDFluid MechanicsAIComputational MethodsDigital TwinRheometerExperimental MethodsMachine Learning

What you'll do

  • Develop a digital twin rheometer for measuring complex fluid parameters.
  • Utilize AI techniques to analyze computational and experimental data.
  • Conduct high-performance computing simulations related to non-Newtonian flows.
  • Collaborate with the Complex Fluids Research Group and other researchers.
  • Engage in training and administrative tasks as permitted.

Requirements

Degree required: Master's degree in engineering, mathematics, or physics with a focus on fluid mechanics

Degree offered: PhD

Language: English; equivalent to English B/6

Instruction: English

Required skills
  • Strong background in fluid mechanics and computational methods
  • Experience with experimental techniques in fluid dynamics
  • Ability to work independently and collaboratively
  • Strong analytical and problem-solving skills
Nice to have
Familiarity with AI and machine learning techniquesExperience in high-performance computing environmentsGood communication and interpersonal skills
Eligibility
  • Candidates must not have resided in Sweden for more than 12 months in the last 3 years
  • Must hold a second cycle degree or equivalent qualifications

Funding & terms

Type
Salaried
Amount
€3.0k/mo
Duration
4 years
Programme
Marie Curie Doctoral Network
EU-FUNDEDEU Marie Curie Actions

How to apply

Deadline: 2026-10-31T23:59:59 (Europe/Stockholm)

Applications must be submitted through KTH's recruitment system. Ensure that your application is complete according to the provided instructions. Include all required documents as specified.

Documents required
  • CV
  • Cover letter
  • Copies of diplomas and grades
  • Certificates of language requirements
  • Representative publications or technical reports
↗Apply on site

Contact

Outi Tammisola· Supervisor

outi@kth.se

OT
Prof.
Outi Tammisola
Complex Fluids Research Group
Lab
Complex Fluids Research Group
KTH Royal Institute of Technology
Marie Curie Doctoral NetworkSweden
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