PhD student in generative modeling for data-efficient machine learning

Linköping University; Norrköpings kommun; On-site; SEK 326,335 - 456,869; Full time

Benefits offered by this job

Diversity and equal opportunities policies

Negotiated salary progression

Job summary

Linköping University in Norrköping is seeking a PhD student to focus on generative modeling for data-efficient machine learning. The position is part of the WASP program, emphasizing research that addresses fairness and privacy.

Ideal candidates will have a Master’s degree in a relevant field and strong programming skills, particularly in Python. The role involves conducting theoretical and applied work, with opportunities for teaching or departmental duties.

Qualifications

• Master’s level education in relevant fields, or equivalent knowledge.
• Fluent communication in oral and written English.
• Strong interest in machine learning and privacy concerns.

Responsibilities

• Conduct research on generative modeling for data-efficient machine learning.
• Participate in the WASP curriculum and collaborate with peers.
• Implement and test machine learning algorithms.

Skills

Programming skills in Python

Knowledge of LaTeX

Version control systems (git)

Understanding of machine learning efficiency

Communication skills in English

Education

Master’s degree in Computer Science, Statistics, Mathematics, or related field

Tools

GNU/Linux systems

Job description

PhD student in generative modeling for data‑efficient machine learning

Norrköping

Reference number LiU-2026-02831

We are now looking for a PhD student in machine learning with a focus on generative modeling and data‑centric strategies for data‑efficient machine learning with considerations for fairness and privacy aspects.

The position is part of the Wallenberg AI, Autonomous Systems and Software Program (WASP). You will take part in the WASP curriculum and benefit from its extensive network of other PhD students and senior researchers.

Machine learning, and in particular deep learning, requires large amounts of data and energy resources for training. At the same time, there are significant challenges in addressing dataset bias and privacy concerns, especially in applications that deal with sensitive data, such as medical diagnosis. The focus of this PhD project is to develop methods for reducing the dataset size without significantly affecting the performance of a model trained on that data, to promote efficient optimization and reduce the computational demands, while at the same time addressing fairness and privacy aspects. The goal is to promote resource‑efficient and trustworthy machine learning in a joint framework.

In your work, you will explore generative modeling for creating synthetic representative datapoints with a high training value, while considering dataset bias and making sure that sensitive information is not leaked from the real dataset. You will work with different types of datasets (from low‑dimensional point sets to high‑dimensional image data) and target different types of applications (e.g., medical imaging). The work will be both theoretically oriented, as well as focused on implementation of experiments with machine learning algorithms for empirical testing.

As a doctoral student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental duties, up to a maximum of 20% of full time.

You have graduated at 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 of which must be in advanced courses (in the areas mentioned above). Alternatively, you have gained essentially corresponding knowledge in another way. It is required that you are able to communicate fluently in oral and written English.

It is considered advantageous if you have solid programming skills in Python, have good knowledge of LaTeX and version control systems (git), and are comfortable working with (remote) GNU/Linux systems. Moreover, it is considered advantageous if you have a strong interest in machine learning efficiency, fairness, and (differential) privacy.

It is strongly advantageous if you have excellent study results and a strong background in mathematics. You are skilled at implementing new models and algorithms in a suitable software environment, with documented experience. You have a strong drive towards performing fundamental research, the ability and interest to work collaboratively. Furthermore, strong communication skills are highly valued.

The project involves both theoretical and applied work.

The employment has a duration of normally four years’ full‑time equivalent. Extension of employment up to five years is based on the degree of teaching and institutional assignment. Further extensions may be granted in exceptional circumstances. You will initially be employed for one year, after which your employment will be renewed for a maximum of two years at a time, depending on your progress through the study plan.

Starting date by agreement.

This position may come to be a security classified position. If so, security screening including a records check will be carried out before any decision on employment is made.

Salary and employment benefits

The salary of PhD students is determined according to a locally negotiated salary progression.

More information about employment benefits at Linköping University is available here.

We welcome applicants with different backgrounds, experiences and perspectives – diversity enriches our work and helps us grow. Preserving everybody's equal value, rights and opportunities is a natural part of who we are.

Read more about our work with: Equal opportunities.

Contact persons

URL to this page
https://web103.reachmee.com/ext/I011/853/main?site=7&validator=d7a66c13be778ef950c393a904293789&lang=UK&rmpage=job&rmjob=29453&rmlang=UK