Open PhD position in machine learning and data mining for lubricant formulation

About the position

The Department of Mechanical and Industrial Engineering has a vacancy for a PhD candidate in machine learning and data mining for lubricant formulation in the Materials Group. We are looking for candidates with a background in one or ideally more of these areas: machine learning, computational science, statistics, data mining, statistical physics, theoretical chemistry, statistical modelling, applied mathematics.

The project deals with the use of data mining methods, especially machine learning, to extract useful information from chemical databases and experimental results. Lubricants are mixtures of a base lubricant and a large number of additives for different functions.  Lubricantion is a complex problem with many different physical and chemical processes occurring on different length scales. As a result, modern lubricant formulation is still based on tweaking existing recipes.  The new products are typically improved by trial and error. This long, expensive and often frustrating process can be boosted with the help of Artificial Intelligence (AI). Formulating lubricants requires gathering information of available chemicals and substances that have the physical and chemical characteristics required for providing a system with the desired friction and wear. Mining in the available data of chemicals that could potentially become the next generation of lubricants requires new tools, methods, and new approaches, radically different from those used until now.

The goal of this project is to develop new tools and methods for formulating new lubricants with potentially environmentally acceptable properties.  There will be interaction with experimental researchers in the same project, who are collecting data, and with collaborators in Germany who work in the field of data mining and machine learning.

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Place of service: Dep. of Mechanical and Industrial Engineering

Application deadline: 27 May 2020

Contact: Associate Professor Astrid S. de Wijn

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