Research Intern: Investigating Forward Learning Methods for Tiny AI Devices
4 settimane fa
Job Description:
At Https:/Www.Energyjobline.Com/Sitemap, we are seeking a highly motivated Research Intern to join our team and contribute to the development of innovative AI solutions for tiny devices. The primary focus of this internship is to investigate and develop forward learning methods tailored for deployment on tiny embedded neural processing units with limited memory and energy budgets.
Key Responsibilities:
- Conduct an in-depth review of state-of-the-art lightweight learning methods suitable for embedded deployment.
- Implement and train lightweight deep learning models using PyTorch to solve computer vision problems.
- Optimize the trained models for deployment on various embedded platforms, considering factors such as memory footprint, computational complexity, and energy efficiency.
- Implement the different updating strategies analyzed during the literature review, comparing the overhead in terms of memory and operations needed.
Requirements:
- Knowledge of machine learning, deep learning, and computer vision.
- Programming languages: Python and C/C++.
- Experience with deep learning frameworks like TensorFlow or PyTorch.
- Familiarity with embedded systems and hardware platforms is a plus.
What We Offer:
- Curricular internship/thesis work; Duration: around 6 months, depending on the candidate's needs and preparation.
- Canteen (except for UniTN students).
- Support for the search for accommodation at the affiliated structures (no allowance).
- Internship in STMicroelectronics to further prove own abilities, with monthly salary, after a 1st preparatory phase in UniTN.
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