University of St. Gallen
1 week ago
PhD Position in Foundation Models and Efficient LLM Training University of St.Gallen in Switzerland
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded PhD position with access to modern compute infrastructure, support for publishing at top-tier conferences, and full supervision and mentoring throughout the PhD.
Country
Switzerland
University
University of St. Gallen

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About this position
PhD position at the Institute of Computer Science (ICS-HSG), University of St.Gallen, Switzerland, in the DS-NLP Lab with Prof. Dr. Siegfried Handschuh.
The project focuses on foundation models and efficient LLM training, including training dynamics of large language models, scaling behavior, optimization, generalization, token weighting strategies, dataset construction, and large-scale data processing. The role also includes teaching support and supervision of student projects and theses.
Applicants should have a master's degree in computer science, machine learning, computational linguistics, or a related field. Strong Python skills are required; experience with PyTorch or TensorFlow/Keras is desirable. Prior research experience or publications in NLP/ML are a plus, as is interest in linguistics and computational linguistics.
This is a fully funded PhD position with access to modern compute infrastructure and support for publishing at top-tier conferences. The post is based at the University of St.Gallen in Switzerland.
Apply online via the HSG jobs portal and include job ID 2800. The posting does not specify a deadline in the text provided.
Funding details
Fully funded PhD position with access to modern compute infrastructure, support for publishing at top-tier conferences, and full supervision and mentoring throughout the PhD.
What's required
Applicants should hold a master's degree in computer science, machine learning, computational linguistics, or a related field. Prior research experience or publications in NLP, ML, or related areas are desirable. Strong programming skills in Python are required; experience with PyTorch or TensorFlow/Keras is desirable. Candidates should have experience or strong interest in training large language models, efficient training techniques such as LoRA, quantization, sparsity, and distillation, scaling laws, training dynamics, evaluation of generative models, dataset construction, and large-scale data processing. A background or interest in linguistics or computational linguistics, especially tokenization and token weighting strategies, is desirable. Applicants should be able to work independently, structure their research, manage time effectively, and contribute to teaching and collaborative research.
How to apply
Apply online and state job ID 2800. Use the provided application link on the HSG jobs portal. For job-related questions, contact Prof. Dr. Siegfried Handschuh or Anja Boxleinter.
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