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PhD or Post-doc in AI deployment on Resource Constrained Training (RCT)

Published
WorkplaceNeuchâtel, North Romandie, Switzerland
Category
Position
DurationTemporary
Occupation rate
100%

The "Integrated and Wireless Systems" Business Unit, based in Neuchâtel, Switzerland is looking for a Phd or Post-Doc in AI deployment on Resource Constrained Training. (RCT)

CSEM mission and values

Our mission is the development and transfer of innovative technologies to the Swiss industry. Our objective is to make an impact on our customers and on society at large in the fields of precision manufacturing, digital technologies and sustainable energy. Our strength is the excellence of our people, about 550 passionate specialists dedicated to innovation and technology transfer. We believe that strong values support the successful development of our organization as well as the harmonious and balanced development of all our employees.

We are

  • A unique place between research and industry at the cutting edge of new technologies
  • An innovative, non-profit, and employee-driven company
  • A dynamic, multidisciplinary, and multicultural environment
  • A solar team focused on enabling solutions to energy challenges for a sustainable world

Description

Your mission

Join our dynamic team of experienced and emerging AI engineers in the role of a PhD/Postdoc position in training neural network on a resource constrained hardware. Collaborate with leading universities such as EPFL or ETH Zurich to develop a groundbreaking thesis/research work, driving innovation and advancing the state-of-the-art in deep learning method deployed on the extreme edge. Be a part of our group’s mission to pave the way for industry technology transfer while actively contributing to our research and development initiatives. Join us in shaping the future of AI.

Your responsibilities

  • Development of algorithms that are more suited for on-chip training, which includes simplifying backpropagation and other complex training algorithms.
  • Preferred focus on recurrent networks.
  • Enables state of art approaches overcoming memory transfer bandwidth.
  • Work on algorithms that can be efficiently executed on limited-resource environments.
  • Model Compression and Simplification:
    • Techniques like quantization, which reduces the precision of the numbers used in the model, and pruning, which removes redundant or non-critical parts of a model.
    • Development of compact neural network architectures that require less computational power for training and inference.
  • Innovation and Research: conduct in-depth research to advance the state-of-the-art in embedded training, exploring novel approaches and solutions.
  • Industry Technology Transfer: Work on projects with the primary goal of transferring developed technologies and innovations to the industry, bridging the gap between academia and practical applications.
  • Collaborate across multi-disciplinary fields specifically S/W & H/W.
  • Team Collaboration: actively engage with our team, sharing insights and contributing to group research and development activities related to Edge AI.
  • Continuous Learning: stay up to date with the latest advancements on field of on chip learning by attending relevant conferences, workshops, and seminars to enhance knowledge and skills in the field.
  • Publication: disseminate research findings by publishing in top-tier academic journals and presenting work at prestigious conferences, contributing to the field’s knowledge and recognition.

We offer

WorkingCSEM means

  • being part of a passionate community
  • incredible flexibility, attractive working conditions, and great opportunities of development
  • benefit from a management style based on trust & feedback and that favors a work-life balance

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity.

Contact and Address

We look forward to receiving your complete application file via (CV, cover letter, certificates & diplomas) our job page.

Preference will be given to professionals applying directly.

CSEM SA, Rue Jaquet-Droz 1, 2002 Neuchâtel

Web

 
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In your application, please refer to myScience.org and reference JobID 2787946.