PhD Position in Machine Learning Systems & Data Science

You will work on your doctoral thesis in a dynamic environment where scientific rigor meets practical relevance. Your goal is not just to apply existing tools, but to advance the field of Machine Learning Systems and Data Science by deriving generalizable insights from complex, real-world data. You connect scientific findings with industry-specific applications. You will design and implement novel scalable machine learning architectures in collaboration with leading international companies to validate theoretical concepts. You design and conduct high-quality research. The clear goal is to publish your findings in top-tier international journals and conferences and contribute to the global academic discourse. You take ownership of research projects, acting as the bridge between abstract methodology and concrete industrial challenges. You engage in teaching at the School of Computer Science (SCS-HSG) as well as in interdisciplinary foundational courses at the University of St.Gallen, a responsibility that is separately compensated. You also co-supervise Bachelor's and Master's theses and projects with a focus on scientific methodology.

The Institute of Computer Science in Vorarlberg (ICV-HSG), a joint initiative with the University of St.Gallen (HSG), acts as a bridge between rigorous academic research and real-world innovation. Located at Campus V in Dornbirn (Austria), we provide an environment where excellence meets entrepreneurial spirit. A place where knowledge is created - As one of Europe's leading universities of economics and business administration, the University of St.Gallen (HSG), Switzerland, is committed to the education of over 10'000 students. The HSG is one of the largest employers in the region and provides an attractive and innovative environment for more than 3'500 researchers, educators and professional staff.

• University-level Master's degree (120 ECTS) in Computer Science, Data Science, or a related field with excellent grades.

• Strong background in Python and deep understanding of ML frameworks (e.g., PyTorch, Keras, Scikit-Learn).

• Solid theoretical understanding of statistics, linear algebra, and the mechanics of machine learning algorithms.

• Comfortable handling complex datasets. Basic knowledge of data engineering workflows or tools for large-scale data is an advantage, but not a strict requirement.

• Basic understanding of Linux-based systems. Experience with containerization (Docker) or HPC is a plus.

• Excellent written and oral communication skills in English for academic writing and presentation. German is desirable but not mandatory.

• Independent and structured way of working, with the ability to manage your time effectively in an entrepreneurial environment.