PhD Student for Sustainable and Resource-Efficient Machine Learning
This PhD project at Linköping University targets sustainable and resource-efficient machine learning, with a focus on methods that reduce compute, energy usage, memory, storage demands, and associated carbon emissions, all while aiming to maintain model quality. The work will involve developing new methodologies and algorithms for resource-efficient learning, potentially via data selection and filtering, and investigating complementary approaches to reduce inference and deployment costs, such as model compression and hardware-aware optimization. Research will also explore how resource-efficiency interacts with broader sustainability aspects of machine learning, including robustness, fairness, and accessibility.
The PhD student will design and run reproducible experiments, measure relevant resource metrics, implement prototypes in Python, and communicate results through publications and research presentations. The exact research direction will be defined jointly with the supervisors. A PhD student devotes most of their time to doctoral studies and associated research projects, with potential for teaching or other departmental duties up to a maximum of 20 per cent of full-time. The project will be carried out in a collaboration between STIMA at Linköping University and the Sustainable Artificial Intelligence for Sciences (SAINTS) Lab at the Department of Computer Science of the University of Copenhagen in Denmark.
The position is based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science at Linköping University, Sweden. Linköping University is recognized as a leading AI institution in Sweden, with strong links to national research initiatives and access to state-of-the-art computing infrastructure for machine learning.
Key Qualifications:
- A Master’s degree in machine learning, computer science, mathematics, statistics, physics, or a related area considered relevant for the research topic.
- Fluency in oral and written English.
- Solid programming skills in Python, good knowledge of LaTeX and version control systems (git), and comfort working with (remote) GNU/Linux systems are advantageous.
- Excellent study results and a strong background in mathematics are strongly advantageous.
- Demonstrated skill at implementing new models and algorithms in a suitable software environment.
- A strong drive towards performing fundamental research, the ability and interest to work collaboratively, and strong communication skills are strongly advantageous.
Deadline: April 24th, 2026