Intern– Reinforcement Learning on Implementation and Design for Control Applications

Job Description

Intern - Reinforcement Learning on Implementation and Design for Control Applications

Are you interested in developing new show cases or technologies for reinforcement learning?

Here’s the right internship opportunity for You!

Join our research group Autonomous Systems and Control located in Princeton, NJ, for a 3 – 9-month internship and develop new technologies for reinforcement learning and its applications to industrial engineering problems. You will be involved in practical demonstration or active research which addresses the challenges of generative engineering design.

Our close contact to different business units in Siemens provides the opportunity to contribute to and gain experience in real industrial problems. 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.

Siemens Background

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.

What are my responsibilities?

  • You will contribute to industrial research projects on reinforcement learning for control applications.
  • You will collaborate and exchange ideas with experts in the fields of reinforcement learning, control theory and optimization.
  • You will develop a show case and control an autonomous ground vehicle (AGV) robot with a policy trained by reinforcement learning. You will learn Siemens’ automation ecosystem where AGV policy can be trained offline and deployed and run online. This position focuses more on practical implementation. You will test and validate your demo on simulation and hardware prototype.
    Position 2: You are given a set of different design choices possibly with different input and output dimensions and internal dynamics. You will develop a reinforcement learning approach to design policies for several designs simultaneously or sequentially fast. Our goal is to learn common representation for this set and develop a reinforcement learning algorithm quickly tuning all design choices. This position requires more advanced machine and reinforcement learning knowledge. You will test and validate your algorithm on a design example.

What skills are needed to qualify for this internship?

  • A current student pursuing a MS/PhD degree in Electrical Engineering or related fields
  • Technical background:
    • Position 1:
      • Familiarity working with robots or similar hardware devices.
      • Good understanding of fundamental reinforcement learning algorithms such as Q learning, DQN, DDPG, TD3, SAC.
      • Familiarity with virtual machines.
    • Position 2:
      • Good understanding of fundamental reinforcement learning algorithms such as Q learning, DQN, DDPG, TD3, SAC.
      • Experience in unsupervised learning for representation extraction.
      • Proficiency in hands-on implementation of reinforcement learning algorithms.
      • Familiarity with large-scale and parallel training for reinforcement learning.
  • Experience in Linux and Git preferred.
  • Good programming knowledge in Python and PyTorch preferred.
  • Experience with Open-Source Reinforcement Learning Frameworks preferred.

·         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.

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

Organization: Technology

Company: Siemens Corporation

Experience Level: Student (Not Yet Graduated)

Full / Part time: Full-time

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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