Onsite Master's Thesis – Modelling Approaches and Loss Design for Precise Age Estimation

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About the role

  • Master's thesis focused on key modeling approaches and loss functions in machine learning at Fraunhofer. Engaging in research, implementation, and evaluation of novel methodologies in age estimation.

Responsibilities

  • Researching and structuring the literature on a current topic in the field of machine learning.
  • Researching and implementing novel machine learning and computer vision approaches.
  • Planning and conducting experiments.
  • Self-critical evaluation of the obtained results.
  • Presenting the results.
  • Preparing a scientific paper in the form of a master's thesis with the results.

Requirements

  • Good knowledge in the field of machine learning and training neural networks
  • Ideally, knowledge in computer vision and facial recognition
  • Good Python skills, preferably experience with PyTorch, OpenCV, etc.
  • Motivation to independently delve into new and current research topics
  • Interest in optimisation and evaluation metrics
  • Interest in scientific research.

Benefits

  • Independent work schedule management
  • Insights into the intersection of academic research and industrial application

Job title

Master's Thesis – Modelling Approaches and Loss Design for Precise Age Estimation

Job type

Experience level

Mid levelSenior

Salary

Not specified

Degree requirement

Postgraduate Degree

Tech skills

Location requirements

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