Working Student Machine Learning for Solver Acceleration in Nürnberg bei Siemens

Mode of Employment: Limited; 15-20 hours/week

Develop what will be important tomorrow.

How will we travel, communicate, and use energy in the world of tomorrow? Work on the right answers with us. We are looking for bright minds – thinkers with a passion for practical matters. In short, people who can turn their questions and ours into exciting new discoveries. When you join our team as one of our working students, you will find many opportunities to get involved. Seize the chance to be part of our global network, make key contacts, and get your career as well as your personal life on the best track. Our teams are curious to meet you.

We make real what matters. This is your role.

• You support the research team “Machine Learning for Solver Acceleration”
• You will be working in a team whose task is to develop the next-generation ML-accelerated solvers for industry applications, e.g. Simcenter STAR-CCM+
• Furthermore you will provide assistance in exploring a variety of Neural Networks architectures, in particular 3D-CNN and Graph Neural Networks for application in a solver-in-the-loop approach

What you need to make real what matters.

• You are currently studying Computer Science, Physics, Maths or similar
• You could already gain experience with computational fluid dynamics and hands-on experience with neural networks
• Communication skills, well-structured and organized approach to tasks are characterizing you
• You have excellent English skills

Make your mark in our exciting world at Siemens.

www.siemens.de

if you wish to find out more about Siemens before applying.

Do you have questions about the application? Here you will find answers to frequently asked questions.
If you have more questions please contact: www.siemens.de/fragenzurbewerbung
www.siemens.com/careers
if you would like to find out more about jobs & careers at Siemens.

As an equal-opportunity employer we are happy to consider applications from individuals with disabilities.