Postdoc in Quality-Aware Machine Learning and AI Systems at KTH Royal Institute of Technology KTH Royal Institute of Technology in Sweden

Postdoc in Quality-Aware Machine Learning and AI Systems at KTH Royal Institute of Technology (School of Electrical Engineering and Computer Science, Department of Computing and Learning Systems).

This is a two-year postdoctoral research position focused on quality-aware representation learning for responsible and sustainable machine learning. The project studies how ML models can learn data representations that support not only predictive performance, but also fairness, privacy, sustainability, and contextual fidelity. The successful candidate will develop and evaluate novel representation learning methods, create benchmarks and evaluation protocols, and collaborate with interdisciplinary researchers and societal stakeholders.

Research areas / keywords: machine learning, AI systems, representation learning, trustworthy AI, fairness, privacy, sustainability, multi-objective optimization, synthetic data, large language models, data science, and critical perspectives on AI.

Eligibility highlights: applicants must hold a doctoral degree (or equivalent foreign degree) in computer science, machine learning, AI, data science, statistics, or a related field by the time the employment decision is made. The post emphasizes independent research experience, peer-reviewed publications, programming and data analysis skills, and strong English communication. Preferred experience includes representation learning, trustworthy AI, interdisciplinary research, open-source/reproducible research, and awareness of diversity and equal opportunity issues.

Funding / employment: the position is a full-time temporary employment with monthly salary. No stipend amount is stated. There are no teaching obligations.

Application window: published 2026-06-10; last application date 2026-07-09.

How to apply: log into KTH's recruitment system and submit the required documents: CV, diplomas and grades, translations if needed, a cover letter (max two pages), and contact information for two to three academic references. Make sure the application is complete before the deadline.