Fraunhofer-Gesellschaft
Masterarbeit: Faire und ausbalancierte Altersschätzung durch dynamisches, gruppenweises Training(m/w/x)
Full-timeInternshipOn-site
Darmstadt
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FRFraunhofer-Gesellschaft
Master's Thesis: Fair and Balanced Age Estimation through Dynamic Group Training(m/w/x)
Darmstadt
Full-timeInternshipOn-site
AI/ML
Data Science
Nejo AI Summary
Developing and evaluating dynamic training strategies for fair age estimation, implementing oversampling and curriculum strategies at application-oriented research organization. Good machine learning and neural network training knowledge required, PyTorch/OpenCV experience preferred. Independent schedule management, insights into research and industry.
Requirements
- Good knowledge in machine learning and neural network training
- Ideally, knowledge in computer vision and facial recognition
- Good Python skills, preferably experience with PyTorch, OpenCV
- Motivation for independent research into new topics
- Interest in robustness and evaluation metrics
- Interest in scientific research
Tasks
- Develop and systematically evaluate dynamic training strategies
- Calculate and use subgroup-specific metrics for control
- Develop and implement dynamic oversampling strategies
- Develop and implement uncertainty sampling strategies
- Develop and implement curriculum strategies
- Examine appropriate aggregation metrics over subgroups
- Compare with classic oversampling, probabilistic sampling, GroupDRO, and JTT
- Explore combinations using AutoML and hyperparameter search
- Evaluate methods using freely available benchmark datasets
- Compare developed methods with existing approaches
- Document code for reusability and reproducibility
- Research and compile information on current ML topics
- Research and implement novel ML and computer vision approaches
- Self-critically evaluate obtained results
- Present the results
- Prepare a scientific paper as a master's thesis
Education
- Currently in higher educationOR
- Bachelor's degreeOR
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
- OpenCV
Benefits
Flexible Working
- Independent work schedule management
Startup Environment
- Insights into research and industry
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Fraunhofer-Gesellschaft and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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FRFraunhofer-Gesellschaft
Master's Thesis: Fair and Balanced Age Estimation through Dynamic Group Training(m/w/x)
Darmstadt
Full-timeInternshipOn-site
AI/ML
Data Science
Nejo AI Summary
Developing and evaluating dynamic training strategies for fair age estimation, implementing oversampling and curriculum strategies at application-oriented research organization. Good machine learning and neural network training knowledge required, PyTorch/OpenCV experience preferred. Independent schedule management, insights into research and industry.
Requirements
- Good knowledge in machine learning and neural network training
- Ideally, knowledge in computer vision and facial recognition
- Good Python skills, preferably experience with PyTorch, OpenCV
- Motivation for independent research into new topics
- Interest in robustness and evaluation metrics
- Interest in scientific research
Tasks
- Develop and systematically evaluate dynamic training strategies
- Calculate and use subgroup-specific metrics for control
- Develop and implement dynamic oversampling strategies
- Develop and implement uncertainty sampling strategies
- Develop and implement curriculum strategies
- Examine appropriate aggregation metrics over subgroups
- Compare with classic oversampling, probabilistic sampling, GroupDRO, and JTT
- Explore combinations using AutoML and hyperparameter search
- Evaluate methods using freely available benchmark datasets
- Compare developed methods with existing approaches
- Document code for reusability and reproducibility
- Research and compile information on current ML topics
- Research and implement novel ML and computer vision approaches
- Self-critically evaluate obtained results
- Present the results
- Prepare a scientific paper as a master's thesis
Education
- Currently in higher educationOR
- Bachelor's degreeOR
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
- OpenCV
Benefits
Flexible Working
- Independent work schedule management
Startup Environment
- Insights into research and industry
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Fraunhofer-Gesellschaft and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
About the Company
Fraunhofer-Gesellschaft
Industry
Research
Description
Das Unternehmen ist eine der führenden Organisationen für anwendungsorientierte Forschung mit 76 Instituten in Deutschland.
Not a perfect match?
- Fraunhofer-Gesellschaft
Masterarbeit: Faire und ausbalancierte Altersschätzung durch dynamisches, gruppenweises Training(m/w/x)
Full-timeInternshipOn-siteDarmstadt - Fraunhofer-Gesellschaft
Master's Thesis: Modelling Approaches and Loss Design for Precise Age Estimation(m/w/x)
Full-timeOn-siteNot specifiedDarmstadt - Fraunhofer-Gesellschaft
Masterarbeit: Modellierungsansätze und Loss-Design für präzise Altersschätzung(m/w/x)
Full-timeInternshipOn-siteDarmstadt - Fraunhofer-Gesellschaft
Master's Thesis: Detection of Suggestive Poses and Facial Expressions(m/w/x)
Full-timeInternshipOn-siteDarmstadt - Fraunhofer-Gesellschaft
Master's Thesis: Detection and Segmentation of Suggestive Clothing(m/w/x)
Full-timeInternshipOn-siteDarmstadt