💡 About the Field:
Data-assisted computational modeling of particulate flows combines cutting-edge machine learning with traditional fluid dynamics simulations to revolutionize how we understand and predict complex industrial processes. This exciting field merges computational fluid dynamics (CFD), discrete element methods (DEM), and deep learning techniques to create real-time digital twins of industrial furnaces and particulate systems. By developing surrogate models and data-driven approaches, researchers can dramatically reduce computational costs while maintaining accuracy, ultimately helping industries reduce energy consumption and CO2 emissions in powder metallurgy and related high-tech manufacturing processes.
🔍 Position Summary:
✅ Funding: Fully funded PhD position with competitive salary
✅ Salary: EUR 3,715 gross per month (paid 14 times per year)
✅ Location: Johannes Kepler University, Linz, Austria (with 6-8 weeks per year at Plansee SE, Reutte)
✅ Work Type: Full-time, Remote options available
✅ Start Date: January 2026 or as soon as possible
✅ Research Focus: Data-assisted computational modeling of particulate multiscale and multiphysics flows
✅ Laboratory: Christian Doppler Laboratory for Data-Assisted Simulations of Complex Flows
✅ Industry Partner: Plansee SE (leading powder metallurgy company)
📋 Requirements:
✅ Master’s degree in Physics, Applied Mathematics, Mechanical Engineering, Computational Science, or related field
✅ Solid background in classical simulation techniques (CFD, CFD-DEM)
✅ Strong programming skills in scientific computing languages
✅ Interest in or willingness to learn deep learning and data-driven modeling methods
✅ Beneficial: Experience with machine learning frameworks and physics-based simulations
✅ Experimental skills for validation experiments
✅ Willingness to conduct on-site visits
✅ German language skills desirable (not required)
📝 Application Documents Required:
✅ Short but honest cover letter detailing research interests and motivation
✅ 2-page CV including relevant publications (if any)
⏰ Deadline: Not specified (apply as soon as possible)
🚀 Step-by-Step Application Guide:
1️⃣ Prepare your cover letter: Write a short, honest cover letter explaining your research interests in data-assisted modeling, computational fluid dynamics, and your motivation for joining this specific project.
2️⃣ Create your CV: Prepare a concise 2-page CV highlighting your academic background, relevant coursework, programming skills, simulation experience, and any publications or research projects.
3️⃣ Submit your application: Send your complete application package (cover letter + CV) via email to andrea.scharinger@jku.at OR apply directly through the Plansee Group careers portal.
4️⃣ Alternative contact: You can also reach out to the recruiting team at Sonja.Fuchs@plansee-group.com for additional information.
🔗 Apply Here – Official Position Page
📧 Email Applications: andrea.scharinger@jku.at
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