Research Assistant I/II on “AI For Sustainability Science”

Applications are invited for appointment as Research Assistant I/II in the Sustainability X-Lab, Department of Real Estate and Construction, to commence as soon as possible for 12 months, with the possibility of renewal subject to satisfactory performance.

Duties and Responsibilities

1. Research Implementation & Computational Modeling

• Develop and implement AI/ML-driven models to quantify sustainability boundary conditions (e.g., planetary boundaries, urban metabolic thresholds) and optimize social-ecological-technical system (SETS) interactions;
• Design geospatial-temporal AI pipelines integrating heterogeneous datasets (remote sensing, IoT, socio-economic surveys) to analyze sustainability transitions across spatial scales (urban, regional, global);
• Apply deep reinforcement learning (DRL) and agent-based modeling (ABM) frameworks to simulate low-carbon transition pathways under climate change constraints;
• Conduct life cycle assessment (LCA)-guided AI optimization for resource efficiency and circular economy solutions; and
• Connect openBIM with life cycle carbon accounting process.

2. Data Curation & System Integration

• Manage multi-source sustainability databases (e.g., climate trajectories, energy flows, ecological footprints) using FAIR principles (Findable, Accessible, Interoperable, Reusable);
• Build knowledge graphs to map causal relationships between SDG targets, policy interventions, and environmental outcomes;
• Automate preprocessing workflows for geospatial informatics (GIS, satellite imagery) and time-series analytics (carbon flux, urbanization rates); and
• Maintain reproducible workflows using Python/R scripting, TensorFlow/PyTorch, and cloud computing platforms (AWS, Google Earth Engine).

3. Model Validation & Uncertainty Quantification

• Perform sensitivity analysis and Monte Carlo simulations to assess AI model robustness under climate scenario uncertainty (SSP-RCP frameworks);
• Validate predictive outputs against ground-truth datasets (e.g., urban carbon inventories, biodiversity indices) using spatial-statistical validation protocols; and
• Develop explainable AI (XAI) frameworks to interpret black-box model decisions for stakeholder engagement.

4. Interdisciplinary Collaboration & Communication

• Collaborate with domain experts in environmental science, urban planning, and policy analysis to co-design transdisciplinary research frameworks;
• Prepare technical reports and visualizations (e.g., Sankey diagrams, scenario dashboards) for grant proposals, publications, and policymaker briefings; and
• Support the lab’s engagement with international consortia (e.g., Future Earth, IPCC) on AI-driven sustainability metrics.

5. Scholarly Dissemination & Impact

• Draft peer-reviewed manuscripts and conference presentations on AI applications in sustainability transition science;
• Contribute to open-source toolkits for AI4SDGs (AI for Sustainable Development Goals) community development; and
• Translate technical findings into policy briefs targeting carbon neutrality roadmaps and urban resilience strategies.

6. Laboratory Operations & Compliance

• Maintain documentation for ethical AI governance (e.g., bias mitigation, data privacy compliance under GDPR);
• Organize lab seminars and workshops on emergent topics (e.g., AI for planetary health, quantum machine learning in sustainability); and
• Assist in procurement and maintenance of high-performance computing (HPC) infrastructure.

Requirements

Academic Qualifications: Applicants should possess a Bachelor’s degree or above in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing or related fields. A solid foundation in computer science basics, including data structures, algorithms, operating systems, and computer networks, is a must. Proficiency in at least one programming language such as Python or Java, along with good programming skills and code standards, is required.

Other Qualifications: Familiarity with the basic principles and architecture of large - language models and their applications in the natural language processing field is essential. Mastery of RAG technology and related tools, and the ability to efficiently retrieve, integrate, and process big data are preferred. Experience in large - model development, including familiarity with the model training, optimization, and evaluation process, and the ability to independently develop the preliminary version of a vertical - category large model is not a must but will be given priority. Knowledge of large - model fine - tuning techniques, and the ability to fine - tune models according to specific task requirements to enhance their performance in particular fields is also needed. And you ought to be familiar with deep - learning frameworks like PyTorch and TensorFlow, and be able to use them for model development and training.

Project Experience: Candidates with relevant project experience will be given priority, such as those who have participated in the development, fine - tuning, or application projects of large - language models, or have practical experience in the natural language processing field. Experience in handling large - scale data sets, and the ability to skillfully use data processing tools and platforms like Hadoop and Spark, and have a certain understanding of big - data storage, management, and analysis is a plus.

Academic Background: You should have a strong interest in the academic research of large models and be willing to explore their academic value in vertical fields at work. The ability to read and understand academic papers in related fields, and write research reports and papers is necessary. Those who have published papers in academic journals or conferences, or have experience in participating in academic projects or research topics, will be preferred.

Other Requirements: Good team-work spirit and communication skills are required, as you need to closely cooperate with team members to complete project tasks. Strong learning ability and problem - solving skills are also important, so that you can quickly master new technologies and knowledge, and independently solve problems encountered in the project. Curiosity and exploratory spirit for new technologies and fields are highly valued, as we expect you to keep learning and trying to promote the innovation and development of the project.

A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits. 

Enquiries can be sent to Prof. Zhang Xiaoling at  zhangxl@hku.hk.

The University only accepts online application for the above post.  Applicants should apply online and upload an up-to-date C.V.  Review of applications will start as soon as possible and continue until August 31, 2025, or until the post is filled, whichever is earlier.  We look forward to your joining and working together with us to make contributions to the field of sustainability research.