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
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