Master Thesis Offer
| Number |
900 |
| Title |
Master Thesis Offer - Model-agnostic Transfer Learning for Regression using Feature Unification Techniques |
| Tasks |
- Based on a real but confidential transfer learning problem (TL) and dataset, to search for similar publically available real world data
- to additionally or alternatively model the relevant problem characteristics to obtain synthetic data of similar structure
- to formalize one or two of a collection of ideas to solve this concrete transfer learning problem
- to implement and evaluate these ideas, and compare with baseline and existing TL approaches
We address creative, self-motivated and conscientious personalities who enjoy solving challenging problems and take responsibility for bringing research projects to success. |
| Requirements |
The successful candidate should have the following skills and attributes:
- Programming competence in Python and/or C++, with experience in visualizations with Mathematica/Matplotlib/Matlab being an advantage
- Basics in machine learning or statistical modeling and regression
- competence in mathematical modeling of problems and solutions
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| Further information |
You will find further information in the attached document |
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