Intern - Reinforcement Learning for System Design & Control System Optimization

Job Description

Are you interested in developing new algorithms for control and decision systems?

Here’s the right internship opportunity for You!

Join our research group Autonomous Systems and Control located in Princeton, NJ, for a 3–6-month internship and investigate and develop new techniques for engineering systems. You will be involved in active research on reinforcement learning (1) with multi-fidelity models and curriculum learning or (2) for discrete/continuous optimization to design system with large action and observation spaces and integrate them to engineering problems. This internship is a remote position.

Our close contact to different business units in Siemens provides the opportunity to contribute to and gain experience in real industrial applications. During this internship, you will experience the excitement and challenges of industrial research. An internship with Siemens Technology is a great opportunity for students to gain real world experience in a diverse work environment.

For 170 years, pioneering technologies, and the business models developed from them, have been the foundation of Siemens’ success. Our central research and development unit, Technology (T) plays an important role in this. Together with its global network of experts, T is a strategic partner to Siemens’ operative units. It provides important services along the entire value chain – from research and development to production and quality assurance, as well as optimized business processes. The support provided to the businesses in their research and development activities is ideally balanced with T’s own future-oriented research. Siemens’ central research and development arm sees itself as a strategic partner to the company’s businesses. It plays a key role in achieving and maintaining leading competitive positions in the fields of electrification and automation while at the same time helping Siemens fully tap into the growth field of digitalization.

Our Princeton facility is recognized for providing a stimulating environment for highly talented and self-motivated students. You will have the opportunity to test your knowledge in a challenging problem-solving environment. You will be encouraged to think out-of-the-box, innovate, and find solutions to real-life problems. Our team has a strong publication record in leading journals and conferences.

The Challenge

  • You will contribute to industrial research projects on leveraging reinforcement learning (1) with multi-fidelity models and curriculum learning or (2) for discrete/continuous optimization to design system with large action and observation spaces and implement them.
  • You will collaborate and exchange ideas with experts in the fields of control systems, machine learning and optimization.
  • You will develop and implement innovative algorithms harnessing modern ML techniques for engineering systems.
  • You will test and validate these algorithms with simulations.

The Candidate

Qualified candidates will have/be:

  • A current student pursuing a MS/PhD degree in Engineering, Computer Science, Mathematics, or related fields.
  • In-depth knowledge of reinforcement learning algorithms and ability to apply them to engineering systems.
  • Experience in for the scope of (1) reinforcement learning with multi-fidelity models and adaptive curriculums, or (2) reinforcement learning for discrete/continuous optimization, Monte Carlo Tree/Graph Search, large action and observation spaces, graphs.
  • Proficient in Python, PyTorch, RL coding environments and agents.
  • Ability to work independently and apply creative thinking strategies for problem solving
  • Excellent team working and communication (verbal & written) skills in English
  • Flexibility and adaptability to work in a growing, dynamic, interdisciplinary team of experts.
  • This position requires employees to be fully vaccinated against COVID-19 unless they are granted a medical or religious exemption

Successful candidate must be able to work with controlled technology in accordance with US Export Control Law. US 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 US Export Control laws and regulations. Additional information on the US Export Control laws & regulations can be found on

About Us

Siemens Technology employees are passionate about applied research. We create technology with purpose that benefits society - more agile and productive factories, more intelligent and efficient buildings and grids, more reliable and sustainable transportation. Our work powers Siemens products and is regularly featured in patents and publications. We operate in a global ecosystem focused on innovation, partnering with our customers, businesses, government agencies, and leading academic institutions on highly visible projects where collaboration is key.

Our people continuously develop their talents, are curious, and are not afraid to take risks in pursuit of technological innovation. A strong learning culture empowers employees at Siemens Technology to own their growth and development.

We know that diversity fuels innovation and drives business success. We are committed to creating an inclusive environment, where diversity of thought, culture, and experience is seen as our greatest strength. This is what brings our best ideas to life.

We take pride in bringing our best selves to work every day, prioritizing individual health, work-life blend, and flexibility.

At Siemens Technology, the success of our employees drives our success.


Organization: Technology

Company: Siemens Corporation

Experience Level: Student (Not Yet Graduated)

Job Type: Full-Time temporary

Equal Employment Opportunity Statement
Siemens is an Equal Opportunity and Affirmative Action Employer encouraging diversity 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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