Postdoctor in deep-learning methods for wall models

Postdoctor in deep-learning methods for wall models

  • KTH Royal Institute of Technology
  • Sweden
  • Salary:

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Job Description

The goal of this project is to develop wall models for turbulent-flow simulations using deep-learning models, with a focus on deep reinforcement learning approaches. The candidate will be co-supervised by Ricardo Vinuesa and Stefan Wallin at KTH Engineering Mechanics. They will join the VinuesaLab research group (www.vinuesalab.com), engaging with projects on large-scale turbulence simulations, deep learning, and experiments. Additionally, they will have access to the multi-petaflops scale supercomputing facilities at KTH, in collaboration with the Swedish e-Science Research Centre (SeRC, www.e-science.se). The researcher will also be part of the excellent research environment FLOW (www.flow.kth.se).

What We Offer

  • A position at a leading technical university focused on sustainable future solutions
  • Engaged and ambitious colleagues in a creative, international, and dynamic environment
  • Work in Stockholm, close to nature
  • Assistance to relocate and settle in Sweden and at KTH
  • Access to the VinuesaLab research group (www.vinuesalab.com), engaging with projects on large-scale turbulence simulations, deep learning, and experiments

Read more about working at KTH

Qualifications

Requirements

  • A doctoral degree or equivalent, met by the time of employment decision
  • Proficiency in English for daily work
  • Relevant publications in fluid mechanics

Preferred Qualifications

  • Doctoral degree obtained within the last three years
  • Degree in Mechanical/Aerospace Engineering or Computer Science with a focus on data-driven or computational methods
  • Relevant degree project
  • International experience
  • Ability to work independently and critically, with good cooperative and communicative skills
  • Research and teaching expertise
  • Awareness of diversity and equal opportunity, with a focus on gender equality

Emphasis will be placed on personal skills.

Trade Union Representatives

Contact information for trade union representatives.

How to Apply

Log into KTH's recruitment system to apply. Ensure your application is complete according to the instructions in the ad. Submit your application by 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 is offered for up to two years. A postdoctoral fellowship is a time-limited, research-focused appointment intended as a first career step after a dissertation.

Additional Information

KTH strives for gender equality, diversity, and equal conditions. 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. This will be communicated during the recruitment process.

We decline all contact with staffing and recruitment agencies and job ad salespersons. Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.

About KTH

KTH Royal Institute of Technology in Stockholm is one of Europe’s leading technical and engineering universities, known for its intellectual talent and innovation. As Sweden’s largest technical research and learning institution, we host students, researchers, and faculty from around the world. Our research and education span natural sciences, all branches of engineering, architecture, industrial management, urban planning, history, and philosophy. Read more here.

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Contact:

  1. Ricardo Vinuesa, Associate Professor, rvinuesa@mech.kth.se
  2. Stefan Wallin, Researcher, stefanw@mech.kth.se

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Job Overview

Postdoctor in deep-learning methods for wall models