Postdoctoral Research Fellow position in synthetic data generation at AI LEARN

UiB - Knowledge that shapes society

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Postdoctoral Research Fellow position

We welcome your application for a postdoctoral research fellow position within privacy-preserving technologies and synthetic data with a particular focus on responsible data sharing and exchange at the Centre for the Science of Learning & Technology (SLATE), Faculty of Psychology. The position is for a fixed-term period of 3 years and is associated with the AI LEARN centre.

AI LEARN

AI LEARN is a new national interdisciplinary and cross-sectoral initiative. The centre aims to understand and shape the human aspects of interaction with and through the use of AI. The goal is to ensure the sustainable, inclusive and responsible integration of AI in both the public and private sectors, to strengthen human agency, and to help everyone participate safely and critically in a society characterised by AI. AI LEARN is a consortium consisting of 34 national and international partners from academia and the public and private sectors.

Six national research centres for Artificial Intelligence were launched in the fall of 2025. These centres will foster interdisciplinary collaboration among researchers to address societal challenges, understand the impact of AI, develop new technologies, and drive innovation across both the business and public sectors. This initiative is established and financed through the government's significant investment in artificial intelligence over the next five years (regjeringen.no) (in Norwegian)

About the project/work tasks:

This postdoctoral research fellow position is associated with Technological Advancements Cluster of the AI LEARN centre, a cluster of projects addressing the critical need for advanced, human-centred AI technologies to empower human learning. The goal of this cluster is to investigate how to evolve advanced, human-centered AI technology to empower human learning, including designing, developing and evaluating systems and models to enhance learning through AI technology.

The postdoctoral research fellow will contribute to the Technological Advancement cluster by advancing synthetic data generation as a key work of AI LEARN’s research for privacy-preserving technologies. The work will involve developing, evaluating, and validating methods for synthetic data generation that enable secure and trustworthy data sharing and exchange for learning applications. The fellow will design approaches that ensure high data utility while rigorously mitigating risks of re-identification and privacy leakage. The work from the postdoctoral research fellow will contribute to enabling public trust using synthetic data and should support case studies for providing our industrial partners with privacy-preserving solutions for testing and improving learning and educational technologies without relying on real data.

You are expected to collaborate with a team (other PhD candidates, postdoc, and researchers) to offer novel perspectives. The postdoctoral research fellow supervisor is Associate Professor Mohammad Khalil. Please contact him for more information about the project.

About the work tasks:

The successful candidate is expected to participate in the centre’s Technological Advancements cluster.

We are seeking a highly motivated individual who wants to excel in a research environment. Part of the main duties and responsibilities include conducting the following tasks:

• develop and evaluate novel models of synthetic data for educational settings
• validate synthetic generation models with AI LEARN partners
• publish research in international peer-reviewed journals, communicate research results and participate in scientific conferences
• contribute to the development of the research environment through active participation in Cluster 2 and contribute to the community at the centre.
• participate in dissemination and communication activities within AI LEARN
• contribute actively to the research environment at the Centre

The successful candidate is expected to live and work in Bergen.

Qualifications and personal qualities:

You must:

• hold a doctoral degree in Computer Science, Artificial Intelligence, Information Security, Learning Analytics, Statistics, or a related field, or have submitted your doctoral thesis for assessment by the application deadline. It is a condition of employment that the PhD has been awarded before employment can commence
• possess strong programming skills, preferably in Python and/or R, and experience with collaborative software development and version control (e.g., Git)
• have strong knowledge of machine learning, statistical learning, data analytics, generative modelling, or related computational methods
• demonstrate research interest in privacy, responsible data sharing, trustworthy AI, or privacy-preserving technologies
• proficiency in both written and oral English
• work independently and in a structured manner, and demonstrate excellent collaboration skills

It is an advantage if you have:

• experience in synthetic data generation, cybersecurity, differential privacy, federated learning, anonymisation, and/or decentralised data sharing models
• experience with AI in Education or Learning Analytics

We can offer:

• Exciting development opportunities as part of your role in a strong professional environment
• Salary as a postdoctoral research fellow (code 1352) in the state salary scale. This currently amounts to an annual salary of NOK 685 900 before taxes. Further salary increases are made according to the length of service in the position. For particularly highly qualified applicants, a higher salary may be considered.
• Enrolment in the Norwegian Public Service Pension Fund
• Good welfare benefits

Your application must include:

• A motivation letter with a brief account of your research interests and motivation for applying for the position (1-2 pages).
• CV.
• Research proposal (maximum 6 pages). The proposal should present an original and clearly motivated research direction within the scope of the postdoctoral position. It should identify a relevant research gap, explain how the proposed work goes beyond existing approaches, define the research objectives, and outline the methodological and both technical and educational approach. Applicants should clearly describe the expected scientific and practical contributions, including what is novel in their proposal and why it matters for synthetic data generation and privacy-preserving data sharing within AI LEARN. The proposal should also explain how the proposed work could be validated with relevant AI LEARN partners. Particular emphasis will be placed on the originality, scientific ambition, methodological soundness, and potential impact of the proposed research. The proposal must reflect the applicant’s own intellectual contribution; proposals generated substantially using Generative AI are discouraged.
• Transcripts and diplomas for master's and doctoral degrees. If you have not completed your doctoral degree, you must attach official confirmation that your doctoral thesis has been submitted for assessment.
• A link to your GitHub (or similar code repository) if available.
• Copies of up to 3 academic publications to be considered in the assessment.
• Complete list of publications.
• The names and contact information for three reference persons; one of these must be the main supervisor for the PhD’s thesis.

The application and appendices with certified translations into English or a Scandinavian language must be uploaded at Jobbnorge