Internship - VLM calibration and finetuning

il y a 3 jours

Courbevoie, Île-de-France IDEMIA Public Security Temps plein

Job description:

IDEMIA is the global leader in trusted identities, with the ambition to empower citizens and consumers to interact, pay, connect, travel, and vote in a connected environment. IDEMIA employs over 15,000 people from more than 80 nationalities and serves clients in 180 countries. The Research Unit at IDEMIA is responsible for developing all new technologies that will be included in all of IDEMIA's product lines. Composed of approximately 100 researchers and developers, the unit works on multiple application areas: facial recognition, fingerprint recognition, iris recognition, fraud detection, secure acquisition systems, video analysis, and document recognition and authentication.

The URT develops algorithms suited for multiple use cases:

  • Identity in the context of civil applications (elections, biometric documents, etc.)
  • Border control (fraud detection on documents or biometrics, facial recognition with comparison to passports and reality, etc.)
  • Law enforcement/Justice (identifying criminals, object identification in videos, etc.)
  • Access control (entry into buildings or any secure area)
  • Digital identity on smartphones (unlocking or payment using a smartphone with biometrics, opening a bank account remotely, etc.)
  • Embedded terminals or smart cards (payment with a biometric credit card, etc.)
  • Road safety (smart radars, etc.)

Key Missions

  • Litterature Review on CLIP-Base models (Siglip, AlphaClip, ...) and the dynamic of their distribution scores
  • Implementing Alignement method and finetuning for improving thresholded accuracy of CLIP-base models
  • Develops test strategies and methodologies to evaluate new or competing algorithms
  • Stays up-to-date on latest state of the art research and algorithm development methodologies
  • Collaborating with researchers to refine methodologies and contribute to the overall project goals
  • Documenting findings and presenting results to stakeholders

Profile description:

Education:
 Engineering student in the final year of the engineering cycle, or a Master's student (M2) specializing in computer vision, image processing, or Deep Learning.

Required Technical Skills:

  • Strong background in mathematics, data analysis, Python, and PyTorch
  • Ability to handle large-scale data and work in a research environment (exploring unfamiliar domains)
  • Knowledge in biometrics is a plus

Soft Skills & Languages:

  • Curious
  • Proactive and autonomous
  • Results- and solutions-oriented
  • Clear and convincing communication
  • Fluent spoken and written English

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