Summary
Linköping University is seeking a highly motivated PhD student in machine learning. This position focuses on generative modeling and data-centric strategies for data-efficient machine learning, with a strong emphasis on fairness and privacy. The role is part of Sweden’s largest individual research program, the Wallenberg AI, Autonomous Systems and Software Program (WASP), offering students access to an extensive curriculum and an international professional network.
PhD Student in Generative Modeling for Data-Efficient Machine Learning, Linköping University, Sweden
Key Details
| Feature | Details |
| Host Institution | Linköping University |
| Country | Sweden |
| Program Level | PhD / Doctoral |
| Duration | 4 Years (extendable up to 5 years) |
| Funding Type | Salaried Employment |
Subject Area
Machine Learning, Computer Science, Statistics, Mathematics, Electrical Engineering, or a related field.
Location
Linköping University, Sweden. The student will be affiliated with both the Division for Media and Information Technology (MIT) at Campus Norrköping and the Division of Statistics and Machine Learning (STIMA) at Campus Valla.
Eligibility
To be considered for this position, applicants must meet the following criteria:
- Education: Graduated at the Master’s level in Computer Science, Statistics, Mathematics, Electrical Engineering, or a related field; OR completed courses with a minimum of 240 credits (at least 60 in advanced courses in the aforementioned areas).
- Language: Fluent in oral and written English.
- Advantageous Skills:
- Solid programming skills in Python.
- Knowledge of LaTeX and version control systems (git).
- Comfortable working with (remote) GNU/Linux systems.
- Strong background in mathematics and excellent study results.
- Interest in machine learning efficiency, fairness, and differential privacy.
- Documented experience implementing new models/algorithms.
Scholarship Description
The primary focus of this PhD project is to develop methods that reduce dataset sizes without significantly affecting model performance. The goal is to promote resource-efficient and trustworthy machine learning by addressing dataset bias and preventing the leak of sensitive information (e.g., in medical imaging). You will explore generative modeling to create synthetic, representative data points with high training value.
The position offers a full-time equivalent employment for normally four years. As a doctoral student, the majority of your time will be devoted to your research project and studies, with a maximum of 20% dedicated to teaching or other departmental duties.
How to Apply
Applications must be submitted through the university’s online portal via the “Apply” button on the official vacancy page. Your application must include the following attachments:
- Cover Letter: (Max two pages) Introducing yourself, your motivation for pursuing a PhD, your interest in the project, how you fit the position, and your preferred starting date.
- Curriculum Vitae (CV)
- List of Publications: (If available)
- Transcripts: Records of both Master and Bachelor studies.
- Scientific Text: A copy (or draft) of your Master’s thesis, or another scientific text such as a Bachelor’s thesis.
- References: Contact details for two references and your relationship to them.
Last Date
August 21, 2026 (Applications and documents received after this date will not be considered).
