Research Institute for Federated Learning
Postdoctoral Fellow
(Ref. 260811008)
[Appointment period: twelve months]
Duties
The appointee will assist the project leader in the research project - “Trustworthy federated learning and AI planning for healthcare applications”. He/she will be required to:
(a) develop trustworthy networked federated learning methods for distributed and heterogeneous healthcare data, including trust modelling, robust collaboration, client selection, privacy protection and personalised federated learning;
(b) investigate one-shot federated learning methods that enable effective knowledge transfer and model collaboration with only one or very few communication rounds;
(c) develop knowledge-transfer and model-composition methods based on model parameters, parameter-efficient fine-tuning modules, prototypes, representations, synthetic data or other transferable knowledge under non-IID data distributions;
(d) study AI planning methods for task decomposition, multi-step reasoning, tool use, agent collaboration, self-reflection and adaptive decision-making;
(e) explore the integration of federated learning and AI planning for distributed intelligent agents and multi-center healthcare applications;
(f) develop and evaluate algorithms using medical images, electronic health records, clinical text or other multimodal healthcare data;
(g) implement research prototypes, conduct experiments, analyse results and prepare technical documentation;
(h) publish research findings in high-quality international conferences and journals, and participate in relevant research projects and academic collaborations;
(i) support the translation of academic research into industrial applications and the preparation of patents and research papers; and
(j) perform any other duties as assigned by the project leader, Director of Unit or their delegates.
Qualifications
Applicants should:
(a) have a doctoral degree or an equivalent qualification in Computer Science, Artificial Intelligence, Software Engineering, Electronic Engineering, Biomedical Engineering, Automation, Applied Mathematics or a related discipline, and must have no more than five years of post-qualification experience at the time of application;
(b) have a solid background in machine learning, deep learning, optimisation, algorithms, probability and statistics or related areas;
(c) have research experience or a strong research interest in one or more of the following areas: federated learning, distributed machine learning, trustworthy artificial intelligence, foundation models, intelligent agents, AI planning, medical image analysis or healthcare artificial intelligence;
(d) be proficient in Python and have practical experience with deep learning frameworks such as PyTorch;
(e) have strong AI-assisted coding capabilities and practical experience in using AI coding tools for code generation, debugging, testing, refactoring and documentation;
(f) have a good proficiency in English and communication skills;
(g) demonstrate strong research motivation, the ability to learn independently and the capability to work both independently and collaboratively; and
(h) be self-motivated, responsible and committed to high-quality research.
Preference will be given to those who have relevant research experience, publication or software development experience.
Applicants are invited to contact Prof. Di Jiang at telephone number 2766 7281 or via email at di-prof.jiang@polyu.edu.hk for further information.
Conditions of Service
A highly competitive remuneration package will be offered.
Consideration of applications will commence on 18 August 2026 until the position is filled.
Posting date: 11 August 2026