PhD student in multiscale multiphysics modeling of laser metal 3D printing

In our Laboratory for Advanced Materials Processing (LAMP), we are currently looking for a

This project is part of our joint efforts with

NCCR MARVEL

 in the computational design of new advanced materials for laser 3D printing applications. At the same time in LAMP, we apply extensive expertise in process design, monitoring, and control as well as state-of-the-art multiphysics modeling and machine learning techniques towards designing Digital Twins for Laser Metal 3D printers, significantly reducing the trial-and-error experimental efforts in simultaneous process<->material optimization (

https://www.empa.ch/web/s204/modeling-simulations

).

Specifically for this project, we are using

Beam 3D printers

 with our world-unique setup of simultaneous injection of metal powder and reinforcement nanoparticles, which allows for local modification and fine-tuning of process-microstructure-property relations during

laser metal deposition

. Thus, multiscale multiphysics modeling approach accelerated by deep learning and supported by benchmark and validation experiments is required for successful execution of this project.

The research is multidisciplinary and requires close collaboration with experimentalists as well as multiphysics modeling and machine learning experts in the lab. We offer a world-class mentorship, an excellent infrastructure, and broad interdisciplinary surroundings with plenty of possibilities for personal and professional development. Due to a high world interest in additive manufacturing of metals and alloys, the research experience acquired in this program will guarantee exciting future opportunities both in academia and industry, nationally and internationally. The work will be carried out at Empa in Thun, next to Bern, Switzerland, and the resulting PhD degree will be issued by EPF Lausanne.

Your profile:

You hold a recent Master degree in computational physics, chemistry, materials science, mechanical engineering, applied mathematics or a closely related field with outstanding grades. Good programming skills in C++ and Python and experience with version control systems and platforms are required for this position. Some experience with any of discrete element, computational fluid dynamics, phase field, molecular dynamics (OpenFOAM, LIGGGHTS, LAMMPS, FLUENT,etc.) as well as Bayesian inference and machine learning would be an advantage.

A strong desire to work at the leading edge of materials technology and a high level of motivation to work in an international, multidisciplinary research team in the field of materials science are essential. Good knowledge of English (oral and written) is mandatory. Knowledge of German or French would be an advantage. The full-time position is limited to 3 years with possibility of extension to 4 years, and is available immediately or upon agreement.