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Research Scientist, Physics x AI
Here at Siemens, we take pride in enabling sustainable progress through technology. We do this through empowering customers by combining the real and digital worlds. Improving how we live, work, and move today and for the next generation! We know that the only way a business thrives is if our people are thriving. That's why we always put our people first. Our global, diverse team would be happy to support you and challenge you to grow in new ways. Who knows where our shared journey will take you?
Transform the everyday with us!
In this role you will work onsite in our Princeton, NJ office.
We are an AI research Lab specializing in Physics x AI research. Our mission is to scale the capabilities of AI in the physical domain, encoding diverse scientific and engineering knowledge into neural networks to tackle complex, high-impact industrial and scientific problems. As an integral part of Siemens' Data Analytics & AI organization, we drive physical AI innovation for an industrial scale.
Our team is part of Siemens Technology, Siemens' central in-house research department. It acts as a strategic partner to support our executive units with R&D and innovation services, protects the company's intellectual property, coordinates collaborations with top universities worldwide, and supports standardization activities in all business units and regions.
As a Physics x AI Research Scientist, you will explore and build the next generation of scalable, generalizable physics-aware foundation models. We actively welcome candidates from a broad range of backgrounds-including fluid dynamics, electromagnetics, electrical engineering, systems engineering, and biological domains such as protein folding-who are passionate about encoding physical, biological, or engineering priors into modern AI systems. You'll focus on architectures (especially transformers) that move beyond the limitations of narrow, hard-coded models into robust solutions that generalize across geometries, conditions, and disciplines.
Your future impact areas may include:

• Systems engineering and verification workflows (from design through manufacturing)
• AI-native electronic design automation (EDA) and chip design
• Any other discipline where physical or biological priors in AI can bring breakthrough scale and generalization

A research scientist in our team will:

• Translate business needs into actionable project proposal, negotiate project terms and budget
• Steer delivery of complex, multi-national, strategic projects in cooperation with stakeholders and the project team
• Validate the market potential of new AI innovation and develop business models for products, services, and solutions.
• Drive ideas from ideation to productization with a customer mindset
• Own global Siemens cross-company technology roadmaps and shape the interfaces to other technologies
• Work with our U.S.-based and global research groups to proactively shape the strategy of our AI research and pre-development operation in the US.
• Shape the subsidies and grants portfolio for our AI research and pre-development in the U.S.
• Communicate complex technology topics to non-experts and executive decision-maker
• Conduct hands-on research and development on scalable, physics-aware foundation models
• Model and encode knowledge from diverse scientific and engineering domains - including but not limited to fluid dynamics, electromagnetics, electrical engineering, biology/protein folding (excluding quantum science)
• Document and publish research, and present proposals and results confidently to both internal and external large audiences
• Proactively engage with Siemens domain experts, their customers and partners to understand real-world problems, collect requirements, and co-create innovative solutions
• Translate cutting-edge research into industrial impact, including proof-of-concept projects, identifying and closing research gaps
• Identify, adopt, and apply evolving trends in machine learning, physics-informed AI, and AI for science
• Collaborate with our interdisciplinary Princeton-based lab and contribute to Siemens' global Data Analytics & AI network

Basic Qualifications:

• Master's degree with relevant research/industry experience or a PhD degree in a STEM field, i.e. Computer Science, Physics, Applied Mathematics, Electrical Engineering, Mechanical Engineering, Biophysics, Bioinformatics, or similar.
• Established track record of publication or patent applications
• Proficiency in one or more modern programming languages, i.e. Python, C++
• Experience with one or more of the following AI programming libraries: PyTorch, JAX, NVIDIA NeMO, HF Transformers
• Proficiency in English both written and verbal
• Authorized to work in the United States without Corporate Sponsorship now or in the future (current Siemens employees on visa will be considered)
• Successful candidates must be able to work with controlled technology in accordance with U.S. Export Control Law. U.S. Export Control laws and applicable regulations govern the distribution of strategically important technology, services and information to foreign nationals and foreign countries. Siemens may require candidates under consideration for employment opportunities to submit information regarding citizenship status to allow the organization to comply with specific U.S. Export Control laws and regulations.

Preferred Qualifications:

• Proven ability to translate ideas from state-of-the-art research into real-world/business impact
• Demonstrated research background with strong publication record (e.g. NeurIPS, ICML, IEEE, AAAI, L4DC) or equivalent industrial research achievements in industrial AI, physics-informed AI, physical AI and/or AI for science
• Demonstrated ability to training neural architectures capable of learning and/or encoding priors from physics, chemistry, biology and/or engineering
• Experience with large-scale, distributed AI/foundation model training
• Fundamental knowledge of software engineering principles and modern software development methodologies
• Eagerness and ability to present research proposals and results confidently to large, diverse technical and business audiences
• Familiarity with AI-based surrogate modelling for CFD, AI-native chip design, protein folding, or verification workflows
• Domain Knowledge and/or experience in the fields of Predictive Maintenance for Industrial Infrastructure, Automation Engineering, Manufacturing, or Electrification are a plus
• Experience participating in international project teams and strong customer orientation
• Proven track record of successfully responding to U.S. government grants is a plus
• Strong knowledge of the Siemens product portfolio is a plus
• Additional language skills, in particular German, are a plus.

About Siemens:
We are a global technology company focused on industry, infrastructure, transport, and healthcare. From more resource-efficient 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.
Our Commitment to Diversity, Equity, and Inclusion:
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. Come bring your authentic self and create a better tomorrow with us.
Protecting the environment, conserving our natural resources, fostering the health and performance of our people as well as safeguarding their working conditions are core to our social and business commitment at Siemens. They are an integral part of our Business Conduct Guidelines and our corporate strategy.
#AIforScience #IndustrialAI #MachinelearningResearch #PhysicsinformedAI
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You'll Benefit From
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 $126,307 - $216,526 annually with a target incentive of 10% of the base salary. The actual wage offered may be lower or higher depending on budget and candidate experience, knowledge, skills, qualifications, and premium geographic location.
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