PhD: Privacy-Preserving Federated Learning & Trustworthy AI

University of Turku; Turku; On-site; EUR 28,000 - 30,000; Full time

Benefits offered by this job

Fully funded position

Publish in top cybersecurity journals

Collaborate with European universities

Job summary

University of Turku invites applications for a full-time Doctoral Researcher position focused on Privacy-Preserving Federated Learning and Trustworthy AI. This role is part of a Horizon Europe project and aims to build a decentralized, privacy-preserving AI infrastructure with explainability features and regulatory compliance.

You will design a Bi-Level FL framework, integrate PETs, and develop an explainable, auditable AI lifecycle. Collaboration with industry and academia is encouraged.

Qualifications

• Eligibility criteria defined in University of Turku Rules of Procedure and the requirement to pursue a doctoral degree.
• Excellent English communication (written and spoken) with certificates such as IELTS or TOEFL.
• Programming skills in Python, C++, Rust and related languages.

Responsibilities

• Design and develop a privacy-preserving Federated Learning framework for trustworthy AI.
• Integrate Privacy Enhancing Technologies (PETs) such as Differential Privacy and secure aggregation.
• Develop a self-learning Bi-Level FL framework with global coherence and local adaptability.
• Create an XAI agent generating human-readable justifications and audit logs for cybersecurity decisions.
• Collaborate with academia and industry partners on a Horizon Europe project.

Skills

Analytical skills

English proficiency

Programming skills

Education

MSc in Computer Science/AI/Cybersecurity

Tools

Distributed computing

Applied cryptography

XAI

Job description

University of Turku invites applications for a full-time Doctoral Researcher position focused on Privacy-Preserving Federated Learning and Trustworthy AI. This role is part of a Horizon Europe project and aims to build a decentralized, privacy-preserving AI infrastructure with explainability features and regulatory compliance.

You will design a Bi-Level FL framework, integrate PETs, and develop an explainable, auditable AI lifecycle. Collaboration with industry and academia is encouraged.