PhD position (m/f/d) for the research area of Machine Learning Systems
Chair of Data Systems
At the university of Bayreuth, Faculty of Mathematics, Physics and Computer Science, at the Chair of Data Systems (Prof. Dr. Ruben Mayer), starting at 01. September 2023, we offer a
PhD position (m/f/d)for the research area of Machine Learning Systems
in full time.
The Chair of Data Systems at the University of Bayreuth, led by Prof. Ruben Mayer, is dedicated to perform high-quality research in the area of large-scale distributed systems for data management, especially in the area of machine learning (ML). We aim to solve real-world problems with data at scale.
- Master’s degree in computer science with very good results
- Interest on topics around the area of distributed systems and data management
- Basic knowledge in distributed systems and machine learning is desired
- Hand-on experience with large-scale data analytics frameworks (Hadoop, Spark, Flink, etc.) or ML frameworks (TensorFlow, PyTorch, etc.) is a plus
- Interest in the development of software systems, very good knowledge and skills in programming with standard programming languages such as C++, Python or Java
- Excellent command of English
- Very good writing skills
- High engagement, high motivation, pro-active communication skills, and high social skills
Machine learning (ML) has enabled major breakthroughs in the development of artificial intelligence in recent years. Two technological trends are fueling these advances: first, the availability of large amounts of data to train ML models, and second, the availability of highly scalable computer clusters (cloud computing) and specialized hardware (e.g., GPUs). Bringing both together in a resource-efficient manner is the goal of our research.
We are driven by the question of how ML systems can become even better. How can we process large amounts of data with less hardware or energy usage? How can we better protect user privacy while providing access to training data? How can we train large ML models in a distributed way to keep up with the big tech corporations? To this end, we are investigating different types of ML systems, for example Deep Learning, Graph Neural Networks or Federated Learning.
We are looking for:
Did we spawn your interest to work in this exciting field and contribute to the progress of science?
To perform this exciting research, we are looking for a highly motivated student who has recently finished / is soon going to finish his/her master’s degree in computer science (or similar). The position is fixed-term according to the “Wissenschaftszeitvertragsgesetz” and serves the own scientific qualification (doctorate) with remuneration according to pay group 13 TV-L. In all other respects, the hiring requirements according to the Bavarian University Innovation Act (BayHIG) apply.
- Work in a highly innovative environment
- Supervision at one of the leading universities of Germany
- Funds for travel and student assistants (HiWis) are available
- We will consider all incoming applications until the position is filled
The University of Bayreuth appreciates the diversity of its employees as an enrichment and is expressly committed to the goal of equal opportunities for all genders. Women are strongly encouraged to apply. Applicants with children are very welcome. The University of Bayreuth is a member of the Best Practice Club "Familie in der Hochschule e.V.", and has successfully participated in the HRK audit "Internationalization of the University". Persons with severe disabilities will be given preferential consideration if they are equally qualified.
We look forward to receiving your application. Required documents: Curriculum vitae, a short letter of motivation, if applicable a list of publications (also blog posts and software projects) and complete transcripts of your Bachelor and Master studies.
Please apply online using the keyword "ML-Systems" via the online application portal of the University of Bayreuth. The documents will be deleted after the position has been filled in accordance with data protection requirements.
If you have any questions, please feel free to contact Prof. Mayer: email@example.com