Computer Vision with Limited Data Research Professional

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 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 research scientist/professional with a focus on in the area of semi-supervised/unsupervised methods for object recognition/pose estimation and semantic segmentation. This role 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: https://arxiv.org/abs/1702.08558

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

https://arxiv.org/pdf/1804.09113.pdf

Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition: https://arxiv.org/abs/1810.04158

Triplet loss with dynamic margin for classification and pose estimation:

http://campar.in.tum.de/pub/zakharov2017iros/zakharov2017iros.pdf

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

https://arxiv.org/abs/1811.07249

End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching: https://arxiv.org/pdf/1802.07869.pdf

Learning without Memorizing: https://arxiv.org/abs/1811.08051

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?

  • Research, design, and implement algorithms that power computer vision algorithms to eliminate the need for large set of labeled training data for deep learning
  • Dive into huge, noisy, and complex real-world computer vision problems to produce innovative analysis and new types of object recognition, pose estimation and synthetic data augmentation methods.
  • Explore the untapped potential of un/semi-supervised techniques for engineering and analysis tasks and devise revolutionary approaches for anomaly detection.
  • Advance the state-of-the-art in the field, including generating patents and publications in top journals and conferences.
  • Develop scalable, customizable and flexible deep learning techniques to real-world problems.
  • Fast prototyping, feasibility studies, specification and implementation for real world challenges.
  • Working with customers to understand algorithm requirements and deliver high-quality solutions.

What skills are needed to qualify for this internship?

  • Candidate must have completed PhD or completed Graduate degree with 3+ years of Experiences in the field of computer vision, machine learning/deep learning.
  • Proven ability to develop new research ideas as demonstrated by a strong publication record and early developments to the level of a working system prototype.
  • Strong theoretical and practical background in pose estimation, object recognition, anomaly detection and semantic segmentation problems.
  • Previous experience in rendering engines/platforms and data augmentation techniques is a plus.
  • Hands-on coding skills and ability to quickly prototype in C++ is a must. Further experience in Scripting languages such as Python.
  • Outstanding written and verbal communication skills in English are required.
  • 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 https://www.bis.doc.gov/index.php/policy-guidance/deemed-exports/deemed-exports-faqs
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Job ID: 182715

Organization: Corporate Technology

Company: Siemens Corporation

Experience Level: Early Professional

Job Type: 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, 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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