Fully Funded PhD in Generative Modeling and Synthetic Data – Linköping University, Sweden (WASP Program)

Linköping University (LiU) in Sweden is recruiting a fully funded PhD student in generative modeling and synthetic data for data-efficient machine learning, with a focus on fairness and (differential) privacy guarantees. The position is part of the prestigious Wallenberg AI, Autonomous Systems and Software Program (WASP) and offers excellent research, training, and career development opportunities in a highly international environment.

Position Snapshot

• Institution: Linköping University (LiU), Sweden

• Departments / Divisions:

  • Division of Media and Information Technology, Campus Norrköping

  • Division of Statistics and Machine Learning, Campus Valla (Linköping)

• Program: Wallenberg AI, Autonomous Systems and Software Program (WASP)

• Level: PhD student (Doctoral Student) – fully employed

• Employment: 4-year full-time PhD position, extendable to 5 years with teaching duties

• Research areas:

  • Generative deep learning (e.g., diffusion models, representation-conditioned generation)

  • Synthetic data generation and data-efficient learning

  • Coreset theory and data summarization

  • Fairness, privacy, and differential privacy in machine learning

  • Trustworthy AI and robust model training

• Supervisors:

  • Dr. Gabriel Eilertsen, Division of Media and Information Technology (Campus Norrköping)

  • Dr. Sebastian Mair, Division of Statistics and Machine Learning (Campus Valla, Linköping)

• Affiliation: Joint affiliation with both divisions; a desk at both campuses

• Start date: By agreement

• Deadline: 21 August 2026

• Application link: https://liu.se/en/work-at-liu/vacancies/29453

• Contact for questions: sebastian.mair@liu.se

About the Research Project

The project focuses on generative modeling and synthetic data for data-efficient machine learning, with explicit attention to fairness and (differential) privacy guarantees. Rather than approximating the model, the idea is to move the approximation to the data: learn small synthetic data summaries that can be used with standard learning algorithms to produce trustworthy models.

Key research activities include:

• Developing and analyzing generative deep learning methods, such as diffusion models and representation-conditioned generation, to create high-fidelity synthetic datasets.

• Combining coreset theory with generative models to construct compact synthetic data summaries that retain essential statistical and structural properties of the original data.

• Ensuring fairness in synthetic data and downstream models, investigating how synthetic data generation can mitigate or propagate biases.

• Incorporating (differential) privacy guarantees into generative modeling workflows, studying trade-offs between privacy, utility, and fairness.

• Working across the spectrum from low-dimensional classical models to high-dimensional deep generative models, developing theory and algorithms that scale to real-world data.

• Evaluating methods on benchmark tasks and real datasets, and comparing with existing synthetic data generation and coreset techniques.

• Publishing research at top machine learning venues (e.g., NeurIPS, ICML, ICLR, AISTATS, AAAI) and presenting work at conferences and workshops.

The project sits at the intersection of machine learning theory, generative modeling, trustworthy AI, and statistical methodology, and is well-suited for candidates who enjoy both algorithmic work and theoretical analysis.

Why This Position Is Attractive (Especially for International Applicants)

• Top-tier research environment: The supervisors and research groups regularly publish at leading machine learning conferences.

• Employment, not a stipend: The PhD position is a full employment contract with monthly salary, pension, healthcare, and paid parental leave.

• Duration and stability: 4 years of full-time research funding, extendable to 5 years when including teaching duties.

• No language barrier: English is the working language in the research environment, and Sweden has a very high level of English proficiency.

• State-of-the-art computing:

  • Access to Berzelius, Sweden's dedicated AI supercomputer, hosted at LiU.

  • LiU will host the upcoming EuroHPC Arrhenius and the MIMER AI Factory, providing exceptional compute resources for large-scale generative models.

• High quality of life: Sweden offers:

  • Five weeks of vacation

  • Flat academic hierarchies and collaborative culture

  • Strong social safety net and work–life balance

  • Easy access to nature, outdoor activities, and a safe environment

• WASP Graduate School: As part of WASP, you will benefit from:

  • Structured coursework and a well-organized graduate school

  • Access to an industrial partner network

  • Possibility of a semester-long international research visit to a leading partner institution

Funding and Employment Conditions

As a PhD student at LiU, you will be fully employed by the university:

• Employment type: Full-time doctoral employment (not a scholarship or stipend)

• Duration: 4 years of full-time research, typically extended to 5 years when including up to 20% teaching or departmental duties

• Salary: Monthly salary according to LiU’s salary ladder for doctoral students, with yearly increments

• Benefits:

  • Pension contributions

  • Public healthcare access

  • Paid parental leave

  • Paid vacation (at least 5 weeks per year)

  • Strong employee rights and protections under Swedish labor law

Exact salary figures and benefit details are provided in the official LiU documentation and may depend on prior experience and collective agreements.

Who Should Apply

This position is ideal for candidates with a strong background in machine learning, statistics, computer science, applied mathematics, or related fields, and a clear interest in generative models, synthetic data, coreset theory, fairness, and privacy.

Academic Background

Applicants should have (or be close to completing) a Master’s degree in one of the following or closely related fields:

• Computer Science

• Machine Learning / Artificial Intelligence

• Statistics / Applied Mathematics

• Electrical Engineering / Information Technology

• Data Science or related quantitative disciplines

Desired Skills and Experience

While the exact requirements are listed in the official ad, strong candidates will typically demonstrate:

• Solid understanding of machine learning and probability/statistics

• Experience with deep learning frameworks such as PyTorch or TensorFlow

• Strong programming skills in Python (and possibly other languages such as C++ or Julia)

• Interest or experience in one or more of:

  • Generative models (e.g., VAEs, GANs, normalizing flows, diffusion models)

  • Coreset theory, data summarization, or active learning

  • Fairness in ML, algorithmic bias, or trustworthy AI

  • Privacy-preserving ML, especially differential privacy

• Ability to work both independently and in a team, and strong communication skills in English

A prior Master’s thesis or research experience in machine learning, generative modeling, or related areas will be considered a strong plus.

Application Materials

Applications must be submitted via the LiU online application system. Incomplete applications may not be considered.

Typical required documents include:

• Curriculum Vitae (CV) – including education, research experience, technical skills, and publications (if any)

• Cover letter / letter of intent – describing your motivation for applying, your research interests, and how your background fits the project

• Copies of degree certificates and transcripts – Bachelor’s and Master’s (or current transcript if the degree is not yet completed)

• Master’s thesis (or draft / summary) – if available

• Names and contact details of referees – usually 2–3 academic references

Check the official vacancy page for the precise list of required documents and any specific formatting instructions.

How to Apply (Step-by-Step)

1. Read the full advertisement

   • Visit the official vacancy page: https://liu.se/en/work-at-liu/vacancies/29453

   • Carefully review the project description, eligibility criteria, and required application materials.

2. Prepare your documents

   • Update your CV to highlight relevant coursework, research projects, and skills in ML, generative modeling, statistics, and programming.

   • Write a tailored cover letter that explains:

     • Why you are interested in this specific PhD position and project

     • How your background and skills match the research topic

     • Your long-term research and career goals

   • Gather transcripts and degree certificates, and ensure they are in English or accompanied by official translations.

   • If available, include your Master’s thesis or a summary/draft.

3. Submit your application via the LiU portal

   • Go to the application link on the vacancy page and create/login to your LiU account.

   • Fill out the online form and upload all required documents in PDF format.

   • Double-check that everything is complete and correctly uploaded before submitting.

4. Submit before the deadline

   • Ensure your application is submitted no later than 21 August 2026.

   • Late applications are typically not considered.

5. Follow up

   • After submission, monitor your email for communication from LiU regarding shortlisting, interviews, or additional information.

   • For specific questions about the position, you can contact sebastian.mair@liu.se.

Why This Opportunity Stands Out

This PhD position offers a rare combination of:

• Cutting-edge research at the intersection of generative models, synthetic data, data efficiency, fairness, and privacy

• Top-tier infrastructure, including access to Berzelius (Sweden’s dedicated AI supercomputer) and upcoming EuroHPC Arrhenius and MIMER AI Factory resources hosted at LiU

• Excellent employment conditions with a full-time salary, pension, healthcare, paid parental leave, and generous vacation

• World-class research environment as part of WASP, Sweden’s flagship AI, autonomous systems, and software program, with structured graduate courses, industry collaboration, and international research visit opportunities

• A high quality of life in Sweden, with flat hierarchies, strong work–life balance, and a welcoming, English-speaking academic culture

For candidates passionate about generative modeling, synthetic data, and trustworthy machine learning, this is an outstanding opportunity to pursue a high-impact PhD at Linköping University within the WASP program.

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