University of Bayreuth in Germany is calling AI & Machine Learning enthusiasts to apply for a Fully Funded PhD Position in the research area of machine learning (ML).
Project Description:
Machine learning (ML) has enabled a breakthrough in the development of artificial intelligence in recent years. Centralized ML systems collect large amounts of data, e.g. from the Internet, and use it to train their models (see ChatGPT, LLAMA, etc.). However, only data that is publicly accessible can flow into the models. In many areas, data protection prevents data access, e.g. for medical and other personal data. To make such private data usable for model training, decentralized machine learning (also known as "federated learning") relies on leaving the data with the data sources (the "clients"). Instead of sharing the data, a local model is trained on the local data on each client and then aggregated on a round-by-round basis to obtain a global model.
There are a lot of challenges to consider. Decentralized training scales less well, as the model parameters must be communicated by many clients via slow (Internet) connections. The distributed architecture enables new attack vectors. In addition, it is unclear how decentralized machine learning complies with AI regulations (especially GDPR and EU AI Act).
The aim of our research is to advance decentralized machine learning in this area and establish it as a powerful alternative to centralized machine learning. To this end, we are conducting research together with our partners (including IBM Research and the University of Cambridge).
Academic Requirements & Qualifications:
Master’s degree in computer science (or related) with very good results
Interest in topics around the area of machine learning, distributed systems, and data management
Basic knowledge in distributed systems and machine learning
Experience with the development of software systems, very good skills in programming with standard programming languages such as C++, Python or Java
Experience with fundamental software engineering concepts such as version control, testing and debugging, CI/CD pipelines, etc.
Hand-on experience with machine learning frameworks (e.g., PyTorch) is a plus
Excellent command of English
Very good writing skills
High engagement, high motivation, pro-active communication skills, and high social skills
Required documents:
Curriculum vitae
A short letter of motivation
If applicable a list of publications (also blog posts and software projects)
Complete transcripts of your Bachelor and Master studies.
Application Deadline: Open until filled
To Read more about this position and to submit your application, visit the official page