Postdoc in machine learning for prediction of turbulent skin-friction

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.

Job description

The postdoctoral candidate will be part the research group Fluids and Surfaces at the Linné FLOW center at KTH. The focus of the research group is on understanding and modelling fundamental mechanisms for the transport of momentum, heat and mass between flowing fluids and materials.

The postdoctoral project aims at enabling the quantitative prediction of skin-friction coefficient using data-driven methods. To achieve this, the student will work closely with other ongoing numerical and experimental projects to generate a robust database of rough-wall turbulence. Neural network and regression models will be used to train a machine learning model that can predict skin-friction coefficient given a set of metrics of the surface roughness. You will work closely with PhD students that work on experimental fabrication of rough surfaces and high-fidelity numerical simulations of turbulent flows over rough walls.

What we offer

• A position at a leading technical university that generates knowledge and skills for a sustainable future
• Engaged and ambitious colleagues along with a creative, international and dynamic working environment
• Works in Stockholm, in close proximity to nature
• Help to relocate and be settled in Sweden and at KTH

Read more about what it is like to work at KTH

Qualifications

Requirements

• A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline (With some exceptions for special reasons such as periods of sick or parental leave, kindly indicate if such reason exists in your resume).

Preferred qualifications

• As a person you are independent, cooperative, problem solving and like to take initiatives.
• We prefer that you possess research expertise and teaching ability, and that you have experience in either of the following subjects: direction numerical simulations, turbulent flows and machine learning.
• It is also considered a merit if you have published in peer-reviewed journals in fluid mechanics.
• It’s commendable if you have proficiency in English, both written and orally, and an awareness of diversity and equal opportunity issues with specific focus on gender equality.

Great emphasis will be placed on personal competency.

Trade union representatives

You will find contact information to trade union representatives at KTH’s webbpage.

Application

Log into KTH’s recruitment system in order to apply to this position. You are the main responsible to ensure that your application is complete according to the ad.

Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).

About the employment

The position offered is for, at the most, two years.

A position as a postdoctoral fellow is a time-limited qualified appointment focusing mainly on research, intended as a first career step after a dissertation.

Others

Striving towards gender equality, diversity and equal conditions is both a question of quality for KTH and a given part of our values.

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Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.

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