Postdoc in deep-learning methods for wall models

Job description

In the climate-neutral transformation of aviation according to "Flightpath 2050", advancements in aerodynamic simulation models are urgent for the necessary design developments with sufficient confidence.

You will be part of the EU research project ROSAS developing wall models for turbulent-flow simulations using and further developing advanced deep-learning methods. In particular, approaches based on deep reinforcement learning.

You will also become part of the VinuesaLab research group (www.vinuesalab.com), having the possibility to interact with several related projects dealing with large-scale turbulence simulations, deep learning and experiments. Furthermore, you will have access to the multipetaflops scale supercomputing facilities available at KTH, in conjunction with the Swedish e-Science Research Centre (SeRC, www.e-science.se). Finally, the researcher will become part of the excellent research environment FLOW (www.flow.kth.se).

The candidate will be co-supervised by Ricardo Vinuesa and Stefan Wallin at KTH Engineering Mechanics.

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
• The researcher will become part of the VinuesaLab research group (www.vinuesalab.com), where he/she will have the possibility to interact with several related projects dealing with large-scale turbulence simulations, deep learning and experiments
• Work in Stockholm, in close proximity to nature
• Help to relocate and be settled in Sweden and at KTH

Read more about what it's like to work at KTH and our benefits.

Qualifications

Requirements

• A doctoral degree or an equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made.
• Proficient in English language since it's used in daily work
• Relevant published work in the field of fluid mechanics 

Preferred qualifications

• A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
• A doctoral degree in Mechanical/Aerospace Engineering or Computer Science with a specialization in data-driven methods and/or computational methods.
• A relevant degree project
•  International experience
• Ability to work independently and perform critical analysis as well as possessing good levels of cooperative and communicative abilities
• Research expertise
• Teaching abilities
• Awareness of diversity and equal opportunity issues, with specific focus on gender equality

Great emphasis will be placed on personal skills.

Trade union representatives

Contact information to trade union representatives.

To apply for the position

Log into KTH's recruitment system to apply for this position. You are responsible for ensuring that your application is complete according to the instructions in 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).

Your application must include:

• Statement of professional interest
• CV with list of publications
• Transcripts from university/university college
• Contact information for at least two references
• PhD thesis and two selected scientific papers (in pdf format)

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.

For information about processing of personal data in the recruitment process.

According to The Protective Security Act (2018-585), the candidate must undergo and pass security vetting if the position is placed in a security class. Information regarding whether the position is subject to such a classification will be provided during the recruitment process.

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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.