Advanced computational modeling is crucial for the efficient design and optimization of industrial processes involving soft materials, ranging from microfluidics to large-scale material processing.

Nick Jaensson is an Assistant Professor in the Processing and Performance of Materials group at Eindhoven University of Technology (TU/e). His research focuses on the development and application of numerical methods for soft materials (also known as complex fluids). Examples of these materials are suspensions, emulsions and polymeric liquids, and they are encountered all around us in everyday life, as well as in many industrial applications. Using advanced numerical methods, and often working with experimental collaborators, Jaensson gains fundamental insights into the flow and transport processes within these materials, and how these are connected to their microstructure. In collaboration with companies, Jaensson works on applying these insights and methods for the design and optimization of industrial processes, ranging from microfluidics to large-scale material processing. Among his current research interests are (interfacial) rheology, non-Newtonian fluid mechanics, uncertainty quantification and physics-informed machine learning.

Nick Jaensson studied Biomedical Engineering at TU/e and received his master's degree in 2012 in the Cardiovascular Biomechanics group of prof. Frans van de Vosse. He switched to the Department of Mechanical Engineering at the same university to become a doctoral student in the Polymer Technology group of prof. Patrick Anderson. He obtained his PhD degree in 2016 with his thesis entitled "Modeling interfaces and particles in viscoelastic fluids". After spending a year in the DSM Materials Science Center in Geleen, the Netherlands, he returned to academia in 2018 as a postdoc in the Soft Materials group of prof. Jan Vermant at the ETH Zürich, Switzerland. As of mid 2020, he is employed as an assistant professor (tenure track) in theProcessing and Performance of Materials group at the TU/e.

Graduation projects available!

If you are interested in non-Newtonian fluid mechanics, finite element modeling, uncertainty quantification and/or physics-informed machine learning, do not hesitate to contact me.