Computer Vision with Limited Data Research Engineer

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 engineer with a focus on in the area of synthetic data augmentation for bridging realism gap. 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?

  • Design and implement deep learning algorithms for computer vision applications (e.g. images and texts).
  • Apply state of the art software engineering principles to develop software applications that demonstrate advanced research results.
  • Contribute to research projects that develop a variety of algorithms and systems in machine learning, multimedia analysis, and natural language processing. 
  • Advance the state-of-the-art in the field, including generating patents and publications in top journals and conferences.
  • Apply image and video analysis techniques such as deep learning to large-scale, real-world problems.
  • Fast prototyping, feasibility studies, specification and implementation of multimedia analysis product components.
  • Working with customers to understand algorithm requirements and deliver high-quality solutions.

What skills are needed to qualify for this internship?

·        Masters Degree in Computer Science, Mechanical Engineering or related discipline. PhD is referred.

  • Good understanding of data structure and algorithms, design and architecture patters.
  • Proficient with C++, Python.
  • Strong understanding of deep learning and computer vision algorithms.
  • Hands one experience with toolkits like OpenCV, VTK, boost, Qt,  Docker, Angular, UML.
  • Familiarity using deep learning tools like Caffee, PyTorch, Tensorflow etc. (working knowledge of deep learning networks and architecture is necessary)
  • Experience working with Windows and Linux environments.
  • Familiarity with cloud technologies and version control system like GIT with continuous integration.
  • Familiarity in salable cross platform Rich client and Server side applications, services
  • 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


Job ID: 182717

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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