The Institute for Machine Learning and the Department of Particulate Flow 
Modelling are looking for a highly motivated PhD student for a joint research 
project with joint supervision. She/he will first carry out high-fidelity CFD 
simulations of turbulent flows and then use the resulting data to train state-
of-the-art deep neural networks. The goal of this project are fast, physically 
sound, long-term predictions of the dynamics and transport behavior of complex 
flows such as submerged single- or multiphase jets.

Job Duties:
• The candidate is expected to conduct research in the intersection of fluid 
mechanics and machine learning.
• She/he will use and further develop open-source CFD tools to generate 
turbulent flow data.
• She/he will employ transformers networks to learn the dynamics in these data 
with a specific focus on physical soundness.
• The research findings should form the basis of a PhD dissertation as well as 
be published in peer-reviewed, international journals, and for conference 
proceedings.

Your Qualifications:
• Diploma/Master’s degree in mechanical engineering, physics, or a related field
• Experience in fluid mechanics and/or numerical simulations (preferably with 
OpenFOAM)
• Programming skills (C++, Python)
• Interest for machine learning (but no extensive experience required)
• High level of commitment and passion for scientific research
• Strong command of English

What We Offer:
• On the basis of full-time employment (40 hours/week) the minimum salary in 
accordance with the collective agreement is € 3,578.80 gross per month (14 x per 
year, CA Job Grade: B1)
• Stable employer
• Attractive campus environment with good public transportation connections
• Attractive continual educational opportunities
• State-of-the-art research infrastructure
• Dynamic research environment in terms of a young, highly motivated team
• Broad range of on-campus dining services/healthy meals (organic food at the 
cafeteria)
• Exercise and sports classes (USI)
• …and much more

Applicants should provide a letter of motivation (max 2 pages) stating why they 
are interested in combining fluid mechanical simulations with deep learning 
techniques and how their educational background will help them to accomplish 
this goal.
This short application is due on September 15th, 2024 and should be sent by 
email (pdf file) to andrea.scharinger@jku.at