Doctoral student in AI-driven quality assurance of manufacturing

3 days ago

Stockholm, Stockholm, Sweden Karlstad University Full time
Doctoral student in AI-driven quality assurance of manufacturing

KTH Royal Institute of Technology is looking for a doctoral student to join the WELD-AID project, a Vinnova-funded initiative focused on improving quality assurance in welded structures through AI and digitalization.

In collaboration with industry partners, the project aims to enhance Sweden's position in welded product design and manufacturing by integrating machine learning (ML) methods to detect weld imperfections and optimize welding processes.

As a doctoral student, you will focus on developing hybrid AI models, including:

  • Convolutional Neural Networks (CNNs) for weld localization
  • Physics-Informed Neural Networks (PINNs) for weld quality assessment and service life prediction

This position is well-suited for candidates interested in applying AI to industrial challenges and advancing welding technology.

Supervision: Professor Joakim Jaldén (Division of Information Science and Engineering) will be the main doctoral supervisor, with support from Professor Zuheir Barsoum (Division of Material and Structural Mechanics) as co-supervisor.

Admission requirements

To be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:

  • passed a second cycle degree (for example a master's degree), or
  • completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or
  • acquired, in some other way within or outside the country, substantially equivalent knowledge

In addition to the above, there is also a mandatory requirement for English equivalent to English B/6.

Selection

In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, candidates will be assessed upon their ability to:

  • independently pursue his or her work
  • collaborate with others,
  • have a professional approach and
  • analyse and work with complex issues.

A successful candidate should have documented good practical and theoretical knowledge of state-of-the-art machine learning methods including deep neural networks and be a proficient programmer, particularly in Python whereas knowledge of lower-level programming languages (C/C++, CUDA) will be considered meritorious.

Knowledge of structural mechanics or materials science is not required (for this particular doctoral position) but will also be considered a merit.

It is advantageous if the candidate can express him- or herself well in Swedish in speech and writing, as this facilitates in the daily work and most of all during cooperation with the Swedish industry.

After the qualification requirements, great emphasis will be placed on personal skills.

What we offer
  • The possibility to study in a dynamic and international research environment in collaboration with industries and prominent universities from all over the world.
  • A workplace with many employee benefits and monthly salary according to KTH's Doctoral student salary agreement.
  • A postgraduate education at an institution that is active and supportive in matters pertaining to working conditions, gender equality and diversity as well as study environment.
  • Help to relocate and be settled in Sweden and at KTH.
Application process

Apply for the position and admission through KTH's recruitment system. It is the applicant's responsibility to ensure that the application is complete in accordance with the instructions in the advertisement.

Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/Central European Summer Time).

Applications must include the following elements:

  • CV including your relevant professional experience and knowledge.
  • Copies of diplomas and grades from previous university studies and certificates of fulfilled language requirements. Translations into English or Swedish if the original document is not issued in one of these languages. Copies of originals must be certified.
  • Representative publications or technical reports. For longer documents, please provide a summary (abstract) and a web link to the full text.
Contact

Prof. Joakim Jaldén: jalden@kth.se
Lisa Olsson: rekrytering@eecs.kth.se

Published: 2025-03-13
Last application date: 2025-03-24

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