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[Sponsors] |
Job Record #19197 | |
Title | PostDoc on Discrete Element Modelling |
Category | Job in Academia |
Employer | Politecnico di Torino |
Location | Italy, Italy, Torino |
International | Yes, international applications are welcome |
Closure Date | Friday, June 07, 2024 |
Description: | |
A Post-Doc position on multi-scale modelling of discrete particle systems is available at Politecnico di Torino (with the Multiscale Modelling for Process Engineering group). We are seeking a highly motivated post-doctoral researcher with expertise in Discrete Element Method (DEM) modeling. The successful candidate will be responsible for conducting simulations of particle packing and particle- structure interactions. This includes analyzing the equilibrium of forces between particles and structures to understand how different packing methods and particle types impact the container filling processes. We are looking for someone with experience in DEM simulations, particle dynamics, and a strong analytical mindset to interpret complex simulation data effectively. The main activity of the project is to employ these methodologies to investigate container filling for applications in the food industry and will be performed in collaboration with a leading food industry company, and provides a unique opportunity to apply DEM modeling to practical industrial challenges. Beyond this, our research group is engaged in a wide array of projects involving computer simulation of packings, from simulating additive manufacturing processes in materials engineering, powders for the pharmaceutical industry [1], to creating packed beds for simulating transport phenomena in chemical reactors [2], energy storage systems [3] and environmental systems [4]. This position offers a dynamic and interdisciplinary research environment with many opportunities for interesting and impactful research. Additionally, our group is at the forefront of integrating machine learning, particularly neural networks, into our research methodologies. While expertise in machine learning is not a requirement, the successful candidate will have the opportunity to engage in multi-scale simulations using advanced machine learning techniques, opening new avenues for research and professional development. If you are passionate about DEM modeling and eager to explore its applications across various industries, we would love to hear from you. Join us to contribute to cutting-edge research and expand your expertise in a collaborative and innovative environment. Application deadline: June 6th 2024 (at 12 PM – CET time) For applications, please follow the instructions at this webpage: https://careers.polito.it/default.aspx?id=104/2024-AR Duration: 1 year, extensions of 1+ years possible. Gross salary: 28.000,00 EUR/yr (corresponding to approx. 2067 € per month) Interested candidates are encouraged to contact: Prof. Daniele Marchisio (daniele.marchisio@polito.it) Prof. Gianluca Boccardo (gianluca.boccardo@polito.it) Qualifications: -Strong interest in physics and modelling of motion of discrete particle systems -Know-how on computational methods for DEM (Discrete Element Methods) simulations For example: Yade-DEM, LAMMPS, LIGGGHTS, CFDEM/Aspherix -Strong interest in coding (Python preferred, C++) and parallel computing -Good knowledge of written and spoken English Previous knowledge on these topics is a plus (but not required to apply): -on machine learning algorithms (especially neural networks) -on transport phenomena and their computational simulation [1] Stratta, L., Adali, M.B., Barresi, A.A., Boccardo, G., Marcato, A., Tuccinardi, R. and Pisano, R., 2023. A diffused-interface model for the lyophilization of a packed bed of spray-frozen particles. Chemical Engineering Science, 275, p.118726. [2] Boccardo, G., Augier, F., Haroun, Y., Ferré, D. and Marchisio, D.L., 2015. Validation of a novel open-source work-flow for the simulation of packed-bed reactors. Chemical Engineering Journal, 279, pp.809-820. [3] Marcato, A., Santos, J.E., Liu, C., Boccardo, G., Marchisio, D. and Franco, A.A., 2023. Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework. Energy Storage Materials, 63, p.102927. [4] Icardi, M., Boccardo, G., Marchisio, D.L., Tosco, T. and Sethi, R., 2014. Pore-scale simulation of fluid flow and solute dispersion in three-dimensional porous media. Physical review E, 90(1), p.013032. |
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Contact Information: | |
Please mention the CFD Jobs Database, record #19197 when responding to this ad. | |
Name | Gianluca Boccardo |
gianluca.boccardo@polito.it | |
Email Application | Yes |
Record Data: | |
Last Modified | 12:35:58, Wednesday, May 29, 2024 |
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