ICME Researcher: Multiscale Modeling & ML for Materials

ICME Researcher: Multiscale Modeling & ML for Materials

Full-Time 40000 - 55000 Β£ / year (est.) No working from home possible
T

At a Glance

  • Tasks: Conduct cutting-edge research in multiscale modelling and machine learning for advanced materials.
  • Company: Join the Technical University of Denmark, a leader in innovative research.
  • Benefits: Engage in impactful projects with EU, NATO, and ESA funding, plus opportunities for professional growth.
  • Other info: Collaborative environment with access to state-of-the-art resources and networking opportunities.
  • Why this job: Make a difference in materials science while mentoring students and sharing your findings globally.
  • Qualifications: PhD in Computational Mechanics and experience with FEniCS, HPC, and machine learning.

The predicted salary is between 40000 - 55000 Β£ per year.

Technical University of Denmark is seeking a Researcher with a Ph D in Computational Mechanics to contribute to EU, NATO and ESA funded projects.

The role focuses on computational modelling, data analysis, and development of novel methodologies for advanced materials and structures.

The postholder will supervise students, publish results, and present at conferences. Experience with FEni CS, HPC and machine learning for materials optimization is desirable.

#J-18808-Ljbffr

ICME Researcher: Multiscale Modeling & ML for Materials employer: Technical University of Denmark

The University is an exceptional employer, offering a supportive and collaborative work environment that prioritises employee well-being and professional growth. With the flexibility of hybrid working and generous leave entitlements, including 36 days off, staff are encouraged to maintain a healthy work-life balance while contributing to the academic success of students in the Faculty of History.

T

Contact Details:

Technical University of Denmark Recruitment Team

We think you need these skills to ace ICME Researcher: Multiscale Modeling & ML for Materials

Computational Mechanics
Computational Modelling
Data Analysis
Machine Learning
FEniCS
High-Performance Computing (HPC)
Methodology Development