Reasoning Graph Representations in Engineering Design - Internship

Position: Reasoning on Graph Representations in Engineering Design - Internship
Job

495819

Posted since

23-Feb-2026

Organization

Foundational Technologies

Field of work

Internal Services

Company

Siemens Corporation

Experience level

Student (Not Yet Graduated)

Job type

Full-time

Work mode

Remote only

Employment type

Fixed Term

Location(s)

* Princeton - - United States of America

Reasoning on Graph Representations in Engineering Design - Internship

We are looking for a talented intern who would like to join our Design and Representation Systems Research Group of the "Simulation and Digital Twin" Technology field for a fixed-term internship.

We need your help to conduct fundamental and applied research in the field of understanding engineering decisions for complex systems. You will be collaborating with leading academic institutions and government agencies as part of this role. Our goal is to model how engineers understand and solve the most complex design challenges by leveraging AI techniques and mimic human reasoning capabilities!

Transform the Everyday with us!

In this role you will work on-site in Princeton, NJ.

Responsibilities:

During this internship, the candidate will experience the excitement and challenges of industrial research and be part of a world class team. This person will:

* Contribute to the task of exploring reasoning over graph representations using graph neural networks.

* Work on the task of exploring application of graph neural networks to engineering design.

* Work on fast-paced co-creation projects with quickly changing requirements in the field at the intersection of machine learning and engineering design.

* Work on low-TRL research projects.

* Work with Siemens software products (e.g., Capital) to develop demonstrators and participate in technology transition to Siemens' businesses.

* Develop demonstrators and participate in technology transition to business.

* Create technical reports.

* Create academic publications.

Required Qualifications:

* PhD students in Mechanical Engineering, Aerospace Engineering, Electrical Engineering, Computer Science, or related discipline.

* Research specialization in the application of AI/ML for engineering design.

* Research specialization in graph neural networks and generative models.

* Familiarity with techniques involving handling large scale graphs.

* Familiar with integration of graph neural networks with knowledge graphs.

* Proficiency in python, PyTorch and PyG.

* English language skill both written and verbal

* Legal authorization to work in the United States without company sponsorship now or in the future.

About Us

We want to provide our customers with Innovative Design solutions and propose intelligent representations for faster and smarter design, engineering, and manufacturing systems. Our team is to become the perfect companions for engineers in need to accelerate by 1000x their workflows. Our mission is to free engineers from the burden of repetitive and non-creative tasks and allow them to truly explore the immensity of their design spaces, efficiently and with confidence in the results.

We want to be "Your cognitive twin", enabling machines to reason and make decisions autonomously.

Our research team is located in beautiful Princeton, NJ, a university town packed with exceptional international talent that provides a unique feel of this true cultural gem in the state. The town has plenty of activities to offer, but for those looking for more, at just about 1h drive we have NYC or Philadelphia. We have the best public schools in the country and all of the above glued together by a very active and welcoming community.

As Siemens' central Research & Development department, we embrace this community. Our core mission is to support our Siemens business units as a central knowledge hub for all cybersecurity capabilities globally. We research and develop new and innovative solutions, based on much needed deep technical expertise, and our network with internal and external experts and academia. This allows us to invent new solutions and approaches and verify their feasibility in the "real world" together with the product development teams of our business units - creating a stimulating setup for quick innovation cycles and rapid prototyping.

Abou…