PhD positions with a focus on Accelerating Rarefied Gas Dynamics

Are you fascinated by fundamental flow physics?  Are you inspired by combining physics-based models with machine learning techniques?  Are you passionate about programming and high-performance computing?  Are you eager to collaborate with industrial partners?

We are looking for a motivated PhD candidate that, combining model-based (physics) and data-driven (machine-learning) approaches, will develop innovative, highly accurate and highly efficient solvers for rarefied gas flows.

Computational fluid dynamics is an essential enabler for science and for many outstanding societal challenges. Many key advanced and emerging technologies require unprecedented control of heat and mass transfer in flows, from continuum to highly-rarefied conditions, often in presence of electromagnetic fields, chemical reactions, and complex interactions with boundaries. Due to the non-equilibrium nature of rarefied flows and the pronounced influence of molecular effects, these transport processes are highly complex and occur in non-standard circumstances.
Contemporary understanding is currently too incomplete to support the development of emerging technologies. Computational modeling is extremely demanding and, in most situations, well beyond foreseeable computing capabilities.
In this project you will break ground on the way rarefied flows are modeled for emerging technologies, by developing innovative approaches that blend data-driven (machine learning) and model-driven (physics-based) methodologies. In this way, you will contribute incorporating the accuracy of computationally expensive atomistic models into macroscopic approaches, while simultaneously severely cutting back the computational cost.
You will work in a consortium consisting of university groups with complementary skills in fluid dynamics and statistical physics (Fluids and Flows, TU/e) and machine-learning techniques (AMLab, UvA), and commercial partners with a need for these new methodologies. ASML, leader in high-resolution lithography solutions, Flow Matters, specialized in consultancy/licensing of rarefied flow solutions, and Carbyon, developing direct-air-capturing systems, contribute with expertise, data and experiments and will be the prime validators and first users of the novel solutions.
The PhD projects will specifically focus on the development of accelerated Direct Simulation Monte-Carlo (DSMC) algorithms by learning from data.

More information  
Do you recognize yourself in this profile and would you like to know more?  
Please contact the hiring manager prof. F. (Federico) Toschi, f.toschi@tue.nl, Department of Applied Physics, group Fluids and Flows https://www.tue.nl/en/research/research-groups/fluids-and-flows/.

Curious to hear more about what it's like as a PhD candidate at TU/e? Please view the video.

Visit our website for more information about the application process or the conditions of employment. You can also contact HR Services, email HRServices.Flux@tue.nl.

Please visit www.tue.nl/jobs to find out more about working at TU/e! 

Application 
We invite you to submit a complete application by using the 'apply now'-button on this page.  
The application should include a: 

• Cover letter in which you describe your motivation and qualifications for the position. 
• Curriculum vitae, including a list of your publications and the contact information of three references.
• Brief description of your MSc thesis. 

We do not respond to applications that are sent to us in a different way. 

Please keep in mind you can upload only 5 documents up to 2 MB each. If necessary, please combine files. 

We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.