PhD Student in Deep Learning (m/f/d)
Veröffentlicht:
25 März 2025Pensum:
100%- Arbeitsort:St. Gallen
Job-Zusammenfassung
Der Lehrstuhl für Künstliche Intelligenz und Maschinelles Lernen (AIML lab) an der Universität St.Gallen ist ein innovativer Forschungsort.
Aufgaben
- Gestalte deine Doktorarbeit im Bereich der Repräsentationslernen.
- Unterstütze die Lehre und betreue Abschlussarbeiten von Studierenden.
- Veröffentliche deine Forschungsergebnisse in internationalen Konferenzen.
Fähigkeiten
- Du hast einen Masterabschluss in Informatik oder verwandten Bereichen.
- Fundierte Kenntnisse in Machine Learning und Deep Learning.
- Erfahrung mit Python und dem PyTorch Deep Learning Framework.
Ist das hilfreich?
The chair of Artificial Intelligence and Machine Learning (AIML lab) is part of the Institute of Computer Science (ICS-HSG) at the University of St.Gallen. Our research focuses on representation learning through supervised and unsupervised approaches with applications to computer vision, remote sensing, or time-series data. You can find more details about our research at www.hsg.ai . In this position you can expect to collaborate within a group of highly motivated individuals in a high velocity research environment at one of the most beautiful places in Switzerland surrounded by the Lake Constance and the Swiss mountains.
Your tasks
The Artificial Intelligence and Machine Learning (AIML) lab headed by Prof. Dr. Damian Borth at the University of St.Gallen (HSG) is seeking candidates for PhD positions in Deep Learning. The position extends the research direction on Weight Space Learning, which was awarded a Google Research Scholar Award 2022 (Machine Learning and Data Mining): https://research.google/outreach/research-scholar-program/recipients/?category=2022 and has been subject of a recent ICLR workshop: https://weight-space-learning.github.io /.
In this position you will design, implement, train, and evaluate deep neural networks and contribute to the varied activities in research and teaching including:
- You will work on your doctoral thesis in the area of representation learning from population of neural networks.
- You will contribute to the current research activities at the AIML lab in the area of representation learning.
- You will publish in international, peer-reviewed conference proceedings and journals.
- You will support the teaching activities of the group, including Taing and the supervision of masters' and bachelors' theses.
Secondary tasks:
- You support the group in knowledge transfer with industry.
- Collaborate with other researchers in interdisciplinary projects.
- You assist the group in organizing workshops, seminars and conferences.
- You engage in outreach activities to promote research and increase the labs its visibility.
- You participate in grant writing and proposal development activities.
Your profile
- You have a university-level master's degree in computer science, statistics, or related studies.
- You have a strong background in Machine Learning and Deep Learning.
- You are confident in the design, implementation, and training of deep neural networks, particularly Transformer architectures.
- You have a strong background in the programming language Python.
- You are experienced with Python and PyTorch deep learning framework.
- You have experience with the NVIDIA GPU platform (DGX is a plus) for large-scale DNN training.
- You have first evidence of publications in peer-reviewed conferences in the respective area.
- You have excellent written and verbal communication skills in English.
Your international mindset paired with your ambition to excel in research and teaching are core qualifications that you bring to our team and projects.
"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 9800 students. The HSG is one of the largest employers in the region and provides an attractive and innovative environment for more than 3300 researchers, educators and professional staff.