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Job Record #19197
TitlePostDoc on Discrete Element Modelling
CategoryJob in Academia
EmployerPolitecnico di Torino
LocationItaly, Italy, Torino
InternationalYes, international applications are welcome
Closure DateFriday, June 07, 2024
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: 

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 ( 

Prof. Gianluca Boccardo (   



-Strong interest in physics and modelling of motion of discrete particle systems 

-Know-how on computational methods for DEM (Discrete Element Methods) 
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. 


Contact Information:
Please mention the CFD Jobs Database, record #19197 when responding to this ad.
NameGianluca Boccardo
Email ApplicationYes
Record Data:
Last Modified12:35:58, Wednesday, May 29, 2024

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