- Princeton - New Jersey - Spojené státy americké
Simulation, Digital Twin, and Physical AI Engineering Internship
Simulation, Digital Twin, and Physical AI Engineer – 6+ Month Internship
Company Overview
Siemens Foundational Technology (FT) is the central R&D organization of Siemens, shaping the future of Simulation, Digital Twin, and AI‑enhanced engineering technologies. Our mission is to accelerate innovation across Siemens’ industry, infrastructure, mobility, and energy businesses.
We are looking for a highly motivated intern to join our Design and Simulation of Systems research group in Princeton, NJ. This team develops next‑generation methods that connect simulation, data, and intelligent physical systems.
In this role you will work remotely from a home office for at least 6 months. (onsite in our Princeton, NJ offices can be a great collaboration experience and would be available if preferred).
Transform the Everyday with us!
Role Summary
In this internship, you will work at the frontier of physics‑based simulation, digital twins, and physical AI—developing methods that allow real‑world assets to perceive, reason, and adapt using both data, physics and models.
Your work will contribute to next-generation industrial capabilities, such as:
- Self‑diagnosing and self‑optimizing industrial equipment
- AI‑enhanced inspection of remote assets (e.g., power infrastructure, ships, offshore platforms, distributed manufacturing equipment)
- Real‑time simulation‑informed decision making
- Closed‑loop digital twins that fuse sensor data and physics models
You will be mentored by experienced researchers and collaborate in an interdisciplinary environment spanning simulation, control, machine learning, and industrial application domains.
Key Responsibilities
- Develop physics‑based and data‑driven digital twin models for mechanical, thermal, structural, or multi‑physics systems relevant to manufacturing and infrastructure.
- Integrate simulation models with AI/ML components to enable perception, anomaly detection, prediction, or control of physical systems.
- Build simulation-to-real pipelines (e.g., domain randomization, synthetic data generation, model‑based RL).
- Design and test workflows for remote asset inspection, including sensor data processing, surrogate modeling, or AI‑based defect detection.
- Benchmark new methods against existing tools, third‑party datasets, or field measurements.
- Collaborate closely with domain experts, simulation engineers, and AI researchers to deliver innovative and practical prototypes.
- Document methods and results, present progress to the research team, and contribute to publications where possible.
Education and Experience
Required
- Currently enrolled in a master’s program in Mechanical, Aerospace, Electrical, Computer, Robotics Engineering, Applied Physics, Computer Science, or other from an accredited university.
- Experience with simulation or modeling tools (e.g., finite element, multibody, CFD, system simulation, or equivalent).
- Hands‑on experience with Python and ML frameworks (PyTorch, TensorFlow, JAX, etc.).
- Familiarity with data‑driven modeling methods such as neural networks, surrogate models, reinforcement learning, or digital twin concepts
- Proficient in English both written and verbal
- Must be located in the U.S. and legally authorized to work in the U.S. for the duration of the internship.
Preferred
- Enrollment in a PhD program.
- Experience with Scientific Machine Learning (SciML), physics‑informed ML, or hybrid modeling.
- Experience with simulation ecosystems (Simcenter, Amesim, MATLAB/Simulink, Modelica, COMSOL, etc.).
- Knowledge of sensor systems used in remote inspection (vision, thermography, vibration, acoustic).
- Experience with C++ or real‑time systems.
- Familiarity with Linux and HPC or GPU environments.
- Ability to learn new tools, technologies, and scientific domains quickly.
- Strong analytical, modeling, and problem‑solving skills.
- Ability to work effectively in an interdisciplinary research environment.
- Strong communication skills (written and verbal).
- Ability to work independently, take initiative, and manage time effectively.
About Siemens:
We are a global technology company focused on industry, infrastructure, transport, and healthcare. From more resourceefficient factories, resilient supply chains, and smarter buildings and grids, to sustainable transportation as well as advanced healthcare, we create technology with purpose adding real value for customers. Learn more about Siemens here.
Our Commitment to Equity and Inclusion in our Diverse Global Workforce:
We value your unique identity and perspective. We are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society, while ensuring that we attract the best talent based on qualifications, skills, and experiences. We welcome you to bring your authentic self and transform the everyday with us.
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Siemens offers a variety of health and wellness benefits to our employees. Details regarding our benefits can be found here: https://www.benefitsquickstart.com/siemens/index.html
The pay range for this position is $35.00-$47.00 per hour. The actual wage offered may be lower or higher depending on budget and candidate experience, knowledge, skills, qualifications and premium geographic location.
Equal Employment Opportunity Statement
Siemens is an Equal Opportunity Employer encouraging inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to their race, color, creed, religion, national origin, citizenship status, ancestry, sex, age, physical or mental disability unrelated to ability, marital status, family responsibilities, pregnancy, genetic information, sexual orientation, gender expression, gender identity, transgender, sex stereotyping, order of protection status, protected veteran or military status, or an unfavorable discharge from military service, and other categories protected by federal, state or local law.
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Applicants and employees are protected from discrimination on the basis of race, color, religion, sex, national origin, or any characteristic protected by Federal or other applicable law.
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