Master Thesis Student for Visual SLAM and 3D Deep Learning (m/f)

Munich, Bavaria, Germany

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Huawei Technologies

Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices.

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Huawei is a leading global information and communications technology (ICT) solutions provider. Driven by a commitment to operations, ongoing innovation, and open collaboration, we have established a competitive ICT portfolio of end-to-end solutions in Telecom and enterprise networks, Devices and Cloud technology and services. Our ICT solutions, products and services are used in more than 170 countries and regions, serving over one-third of the world's population. With 180,000 employees, Huawei is committed to develop the future information society and build a Better Connected World.

Huawei's Munich Research Center is responsible for advanced technology research, architectural development, design and strategic engineering of our products.

The Audiovisual Technology Team in Huawei’s Munich Research Center is responsible for advanced technology research, system solution/ algorithm/ software architecture design and strategic technical planning. This is a key position within the department and requires a person with demonstrated track record of leading research teams, patent generation and a recognized industry technology expert. The team goal is to enable ordinary consumers to create masterpieces. This Team will play a leading role in researching new technology and delivering specific solutions for next generation products including mobile devices, cloud services, IOT, etc.


Now we are looking for a:

Master Thesis Student for Visual SLAM and 3D Deep Learning (m/f)

Responsibilities

  • Investigate the state-of-the-art technology and solutions focusing on the intersection of 3D deep learning and visual SLAM
  • Design and prototype new techniques for real-time indoor visual mapping, and proof of concept on a simulated environment
  • Test and evaluate the developed system in a realistic environment, analyze and address the potential problems accrued during the experiments
  • Generate papers and technical reports if possible

Requirements

  • You are currently registered in a European University to undertake Master of Science (MSc) in the field of Computer Science, Autonomous System, Photogrammetry, or another related discipline
  • You have a deep interest in the topics related to deep learning, or computer vision, SLAM or 3D visual sensing
  • Profound knowledge and experience of Deep Learning, Computer Vision, and Robotics techniques
  • Good programming experience in Python, OpenCV, and TensorFlow or Pytorch
  • Interested in solving challenging problems, self-driven for independent and goal-oriented research
  • Fluent in written and spoken English – German as a plus


By applying to this position, you agree with our RECRUITMENT PRIVACY STATEMENT. You can read in full our recruitment privacy statement via the link below.

http://career.huawei.com/reccampportal/portal/hrd/weu_rec_all.html


What you can expect

  • Our culture is characterized by innovative power and team spirit as well as the intensive exchange of knowledge and experience within our global network.
  • We offer you a competitive compensation package and a broad range of training opportunities. Many online and face-to-face training programs.
  • Self-responsible work in a competent, motivated and constantly growing team.

If you are enthusiastic in shaping Huawei’s Munich Research Center together with a multicultural team of highly skilled Engineers and Researchers, feel free to contact us. Driving future technologies focused on the customer experience is our main mission. Apply now!


Please send your application and CV (incl. cover letter and reference letters) in English.

Tags: Computer Science Computer Vision Deep Learning Engineering OpenCV Python PyTorch Research Robotics SLAM TensorFlow Visual SLAM

Perks/benefits: Career development Competitive pay

Region: Europe
Countries: Germany United States
Job stats:  25  1  0

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