Master Thesis in Continual Pre-Training of Neural Networks
Master Thesis in Continual Pre-Training of Neural Networks
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Job Description
Are you ready to dive into the exciting world of artificial intelligence and machine learning Join us on a thrilling journey as we explore the cuttingedge field of Continual Learning!
- During your thesis you will conduct research on Continual Learning and on (selfsupervised) pretraining methods. Discover how these methods can enhance the performance of deep neural networks and contribute to the development of smarter systems.
- You will get handson experience by implementing and adapting stateoftheart research methods using PyTorch.
- Furthermore you will put your skills to the test as you train deep neural networks on various data types with Python/Pytorch.
- Finally you will learn how to analyze and visualize datasets using Python transforming raw data into compelling stories that highlight your findings and showcase your results.
Qualifications :
- Education: Master studies with a completed bachelors degree in the field of Computer Science (Applied) Mathematics or comparable
- Experience and Knowledge: indepth experience in Python programming with PyTorch Git and machine learning; experience with software engineering and code versioning is an advantage
- Personality and Working Practice: you demonstrate motivation by proactively taking on tasks developing solutions independently and approaching problems critically and analytically to make informed decisions
- Work Routine: for onboarding a oneweek presence in Renningen is required; after that a minimum of one visit per month for three consecutive days is desired
- Enthusiasm: for machine learning
- Languages: fluent in English and very good in German
Additional Information :
Start: according to prior agreement
Duration: 6 months
Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.
Need further information about the job
Tobias Schlagenhauf (Functional Department)
49 7
Pascal Janetzky (Functional Department)
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
