PhD position on Characterizing input composition by statistical learning

Katholieke Universiteit Leuven
October 22, 2022
Offerd Salary:Negotiation
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Contract Type:Other
Working Time:Full time
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PhD position on Characterizing input composition by statistical learning

(ref. BAP-2022-625)

Last modification : Wednesday, August 17, 2022

At the Department of Computer Science of KU Leuven, the Numerical Analysis and Applied Mathematics research unit NUMA works on numerical methods, algorithms and software for simulation and data analysis, with applications in many fields in science and engineering. The research in NUMA focuses, amongst others, on simulation, optimization, data science, uncertainty quantification and high performance computing. The present industrial linked PhD position, which will be carried out in close contact with ArcelorMittal Belgium, fits in a 4-year project funded by the Horizon Europe Framework Program.

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In the steel production process, two important ingredients are combined: pig iron (or crude iron) and scrap (recycled metal materials). Depending on the scrap quality, which is determined by the concentration of impurities such as sulfur, the amount of scrap going into a single production batch can be adjusted. By better characterization of scrap input impurities it is possible to reduce CO2 emissions by using less pig iron.

The aim of this PhD position is to develop methods and algorithms to model the distributional properties of sulfur and other impurities in the different scrap piles, based on available measurements which are present in the current steel production process. For this, you will be in close contact with modelling specialists from ArcelorMittal Belgium. You will apply a number of state-of-the-art methods and algorithms and determine which method is the most suitable to track the interesting properties of the scrap piles and include ways to manage their uncertainties. This will involve Markov Chain Monte Carlo and Gaussian process techniques, whose efficiency will depend on the quality and the features of the data. Validation of the methods can be performed by effective measurements on the scrap piles.

A second part of the work is centered around time dependence of the stochastic model. Since scrap piles are dynamic and their properties change over time, it is necessary that the stochastic distributions of the impurities in the scrap piles can be adjusted over time. An additional difficulty with time dependent modelling is the difference in rate at which scrap piles change and the rate at which information from the production process flows back through the model.

  • You have a Masters degree in Mathematics, Applied Mathematics, Computer Science, Engineering or Physics.
  • You have experience in programming and a general interest in the development of numerical methods and their application in engineering.
  • Excellent proficiency in English is required, both oral and written.
  • Offer
  • We offer a full-time position as a doctoral researcher for four years.
  • You will receive a salary or a monthly tax-free grant. In addition, health insurance and social security will be covered.
  • You will work in a supportive multi-disciplinary research team of engineers and scientists with backgrounds in scientific computing, computer science, mathematical engineering and data science.
  • You will from the start have the opportunity (and are encouraged) to be actively involved in an industrial environment by partly working on-site at ArcelorMittal Belgium.
  • You will build up research and innovation skills that are essential for a future career in research and development, both in industry and academia.
  • Interested?

    For more information please contact Prof. dr. ir. Dirk Nuyens, tel.: +32 16 37 35 59, mail: or Mr. Ward Melis, tel.: +32 16 32 06 16, mail:

    KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at

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