Computer Vision with Limited Training Data Internship

Job Description


Are you interested in empowering machines with better than human perception and cognition skills while solving real industry problems?


Here’s the right internship opportunity for You!

Join our Vision Technologies and Solutions Group (VTS RG) to develop solutions to real-world computer vision problems where there is limited amount of training data for your machine learning algorithms. The CT Simulation and Digital Twin Technology Field (SDT TF) is seeking a highly motivated Master/PhD student available for an internship in the area of semi-supervised/unsupervised methods for object recognition/pose estimation and semantic segmentation. The project will involve analysis of state-of-art in academia and industry, and design of novel practical techniques to address challenging problems in autonomous systems such as autonomous driving trains or autonomous robots.


Vision Technologies and Solutions Research Group, is leading multiple projects as part of the Defense Advanced Research Projects Agency (DARPA) Physics of AI and Automatic Scientific Knowledge Extraction program, to advance the computational tools to address the large training data needs for computer vision applications using deep learning. It also partners with top US universities in projects funded by agencies like Office of Naval Research and National Institute of Food and Agriculture to advance the activity recognition and anomaly detection technologies for various industrial applications.

Our team has a strong publication record in leading journals and conferences and here are some of our example publications together with our previous interns and collaborators:

Depth Synth:

Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only:

Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition:

Triplet loss with dynamic margin for classification and pose estimation:

Learning Local RGB-to-CAD Correspondences for Object Pose Estimation:

End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching:

Learning without Memorizing:

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. Our close contact with business units in Siemens such as Siemens PLM Software, Inc. provides the opportunity for you to contribute and gain experience in real industrial applications. During this internship, you will experience the excitement and challenges of tackling real world problems faced by Siemens and our customers. An internship with Siemens Corporate Technology is a great opportunity for students to gain real world experience in a diverse work environment.

What are my responsibilities?

  • You will contribute to research projects that develop data synthetization and domain adaptation techniques for computer vision applications.
  • You will collaborate an exchange ideas with experts in the fields of pose estimation, anomaly detection and object recognition to develop scalable, customizable and flexible deep learning algorithms to improve our visual perception sdk.
  • You will implement concepts, perform prototyping, feasibility studies, specification and implementation of representation learning techniques
  • Apply developed techniques to real-world problems
  • Help advance the state-of-the-art in the field, including generating patents and publications in top journals and conferences

What skills are needed to qualify for this internship?

  • A current Master/PhD student in Computer Science or related discipline for research 

·       1 years of experience in advanced algorithm prototyping in computer vision, machine/deep learning, or related fields.

  • At least 2 years programming experience in mainstream programming languages like  C++ and Python, with hands-on coding skills and ability to quickly prototype algorithm
  • Experience with parallel programming on GPU preferred.
  • Knowledge in pose estimation and navigation for mobile robots is a plus
  • Excellent team working and communication (verbal & written) skills
  • 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


Job ID: 180762

Organization: Corporate 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, marital status, family responsibilities, pregnancy, genetic information, sexual orientation, gender expression, gender identity, transgender, sex stereotyping, protected veteran or military status, and other categories protected by federal, state or local law.

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Applicants and employees are protected under Federal law from discrimination. To learn more, Click here.

Pay Transparency Non-Discrimination Provision
Siemens follows Executive Order 11246, including the Pay Transparency Nondiscrimination Provision. To learn more, Click here.

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