1 PhD position: Accelerating Rarefied Gas Dynamics using the Method of Moments
5 dagen geleden
Job description
Computational fluid dynamics is a vital tool for scientific research and addressing significant societal issues. It’s indispensable for advanced and emerging technologies that need precise control of heat and mass transfer in flows, ranging from continuum to highly rarefied conditions. These flows often involve electromagnetic fields, chemical reactions, and complex boundary interactions. The non-equilibrium nature of rarefied flows and the significant impact of molecular effects make these transport processes highly intricate and non-standard. Our current understanding is insufficient to support the growth of emerging technologies. Computational modeling is extremely demanding and, in most situations, well beyond foreseeable computing capabilities.
We aim to revolutionize the modeling of rarefied flows for emerging technologies by developing novel approaches that merge data-driven (machine learning) and model-driven (physics-based) methodologies. Our goal is to integrate the precision of computationally intensive atomistic models into macroscopic approaches, while drastically reducing the computational cost.
This PhD project aims to develop a fast, accurate multiscale modeling paradigm that bridges continuum and molecular transport regimes. The goal is to create a Neuro Generalized Method of Moments (NGMoM) model that efficiently models gas-gas collision and gas-solid scattering kernels. This model will incorporate detailed molecular interactions using machine learning algorithms to infer collision kernels from molecular dynamics experiments. The derived kernels will be used to formulate boundary conditions and collision operators for the MoM, ensuring the preservation of fundamental thermodynamic properties. This approach will enable efficient prediction of rarefied transport phenomena in complex engineering domains.
This PhD position is embedded in a consortium with 2 other PhD students and a Postdoc. The consortium consists of university groups with complementary skills in fluid dynamics, statistical physics, and machine-learning techniques, and commercial partners with a strong need for these new methodologies.
Job requirements
A talented, motivated, and enthusiastic researcher. Analytical skills, initiative, and creativity are highly desired. An MSc degree in Mechanical Engineering, Applied Mathematics, Computer Science, Physics, or equivalent degree. A strong background in applied mathematics, (computational) physics and/or mechanical engineering is required. Excellent oral and writing skills in English.
Conditions of employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend max/.10% of your employment on teaching tasks. Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539). A year-end bonus of 8.3% and annual vacation pay of 8%. High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process. An excellent technical infrastructure, on-campus children's day care and sports facilities. An allowance for commuting, working from home and internet costs. A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.-
Eindhoven, Noord-Brabant, Nederland Eindhoven University of Technology VoltijdJob description Are you inspired by combining physics-based models with machine learning techniques? Are you fascinated by fundamental flow physics? Are you passionate about programming and high-performance computing? Are you eager to collaborate with industrial partners? We are looking for two motivated PhD candidates that, combining model-based...
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Eindhoven, Nederland Eindhoven University of Technology VoltijdJob description Are you inspired by combining physics-based models with machine learning techniques? Are you fascinated by fundamental flow physics? Are you passionate about programming and high-performance computing? Are you eager to collaborate with industrial partners? We are looking for two motivated PhD candidates that, combining...
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