Chair Professor/Professor/Associate Professor/Assistant Professor (Artificial Intelligence and Machine Learning for Environmental Engineering) in the School of Energy and Environment [Ref.R/348/26]

City University of Hong Kong (CityUHK) is one of the world’s leading universities, known for innovation, creativity and research.  We are now seeking exceptional scholars to join us as Assistant Professors/Associate Professors/Professors/Chair Professors (on substantiation-track) in all academic fields with special focuses on One Health, Digital Society, Smart City, Matter, Brain, and related interdisciplinary areas.  Research fields of particular interest include, but not limited to:

• biomedical science and engineering

• veterinary science

• computer science and data science

• neuroscience and neural engineering

• bio-statistics and AI-healthcare

• smart/semi-conductor manufacturing

• AI/robotics/autonomous systems

• aerospace and microelectronics engineering

• energy generation and storage

• digital business and innovation management

• fintech and business analytics

• computational social sciences

• digital humanities

• digital and new media

• law and technology

• private law

• healthy, smart and sustainable cities

Successful candidates should have a demonstrated ability to build a world-class research programme related to CityUHK’s strategic research areas, plus a commitment to education and student mentorship.  Candidates must possess a doctorate in their respective field by the time of appointment.

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Applications and nominations are invited for :

Chair Professor/Professor/Associate Professor/Assistant Professor (Artificial Intelligence and Machine Learning for Environmental Engineering) in the School of Energy and Environment [Ref.R/348/26]

The School of Energy and Environment at City University of Hong Kong invites applications and nominations for multiple faculty positions at the levels of Chair Professor, Professor, Associate Professor, and Assistant Professor as part of a strategic cluster hire in artificial intelligence (AI) and machine learning (ML) for environmental engineering and environmental problem-solving.

Established in 2009, the School of Energy and Environment has a mission to advance interdisciplinary research and education in sustainability, energy, environment, and climate, and to cultivate future leaders who can deliver innovative solutions for a rapidly decarbonizing, resilient world.

The School is seeking outstanding scholars who are committed to making internationally recognized contributions to fundamental and applied research, educating the next generation of environmental leaders, engaging with industry and society, and contributing to policy and technological innovation. Successful candidates are expected to develop internationally leading, externally funded research programmes and contribute to excellence in teaching, research student supervision, knowledge transfer, and service.

Areas of Recruitment

Candidates should have a strong background in artificial intelligence, machine learning, data science, or related disciplines, with demonstrated relevance to environmental engineering and environmental challenges. Priority areas include:

• Environmental monitoring, sensing, data integration, and interpretation

• Data-driven and physics-informed modelling, digital twins, process optimisation, automation, and intelligent control of environmental infrastructure and treatment processes

• Applications of artificial intelligence in biological treatment, including wastewater treatment, resource recovery, microbial processes, and bioprocess optimisation

• Machine learning and data-science approaches for water and air treatment, including contaminant detection, treatment-process design, operational optimisation, and performance prediction

• Intelligent waste management, circular-economy systems, waste characterisation, sorting, recycling, and resource valorisation

• Air-quality modelling, source apportionment, exposure assessment, forecasting, and decision-support systems

• Computational and data-driven modelling of pollutant fate, transport, transformation, and environmental risks across air, water, soil, and urban systems

• AI-supported environmental policy, risk assessment, climate and sustainability applications, and smart-city environmental management

Strategic Role of the Positions

The successful candidates are expected to establish or lead vigorous, externally funded research programmes and provide excellence in teaching at undergraduate and postgraduate levels. They will be encouraged to work closely with colleagues across the School to build interdisciplinary research and education activities in environmental engineering, sustainability, energy, climate, and related areas.

In particular, the appointees should demonstrate strong potential to collaborate with existing School faculty and contribute to research in areas including biological treatment; water and air treatment; waste management and resource recovery; air-quality modelling; pollutant transport and environmental fate; environmental monitoring; environmental chemistry; and sustainable urban and industrial systems. Such collaboration may integrate AI/ML methods with laboratory experimentation, field observation, environmental sensing, process modelling, mechanistic analysis, and real-world demonstration.

The appointees will also have opportunities to pursue interdisciplinary research with relevant City University of Hong Kong units and external partners, including the State Key Laboratory of Marine Environmental Health.

Duties :

1. Conduct internationally competitive and impactful research in AI, ML, data science, and their applications to environmental engineering and environmental problem-solving;

2. Develop innovative research directions and secure external research funding;

3. Publish high-quality research outputs;

4. Teach undergraduate and postgraduate courses;

5. Supervise master’s and doctoral students;

6. Engage in interdisciplinary collaboration within the School and across the University;

7. Contribute to knowledge transfer, industry collaboration, professional service, and community engagement; and

8. Undertake administrative duties to support the development and operation of the School.

Requirements :

Applicants should possess a PhD in artificial intelligence, machine learning, data science, computer science, environmental engineering, chemical engineering, civil engineering, systems engineering, statistics, or another closely related discipline.

Applicants should demonstrate a strong and relevant research record or research potential in applying AI/ML, data science, computational methods, or intelligent systems to environmental problems. Evidence may include peer-reviewed publications, research grants, patents, software, data platforms, models, demonstrators, industrial collaborations, or other research outputs. Candidates should also demonstrate teaching ability and a commitment to interdisciplinary collaboration.

For Chair Professor/Professor: Candidates should have outstanding research track records, an international reputation in the field, and demonstrated academic leadership experience. They should be able to provide strategic leadership in building internationally visible research programmes and interdisciplinary collaborations in AI-enabled environmental engineering and sustainability.

For Associate Professor: Candidates should have a sustained record of high-impact publications and successful grant acquisition and should demonstrate the ability to lead and develop an independent research programme. They should have established research expertise in AI/ML, data science, or a related discipline, with a clear and substantive track record of environmental application or strong potential to develop this direction.

For Assistant Professor: Candidates should demonstrate potential for high-impact publication and competitive grant capture. They should articulate a clear plan to establish an independent research programme at the intersection of AI/ML or data science and environmental engineering. Evidence of high-quality publications, relevant technical or research outputs, early grant success, interdisciplinary collaboration, and/or experience in translating AI methods to practical environmental applications will be favourably considered.

Candidates are encouraged to describe their contributions and future plans related to interdisciplinary collaboration, diversity, equity and inclusion, knowledge transfer, industry engagement, and societal, policy, or environmental impact.

Salary and Conditions of Service 

Remuneration package will be driven by market competitiveness and individual performance.  Excellent fringe benefits include gratuity, leave, medical and dental schemes, and relocation assistance (where applicable).  Initial appointment will be made on a fixed-term contract.

Information and Application

Further information on the posts and the University is available at http://www.cityu.edu.hk, or from the School of Energy and Environment at School job openings.

To apply, please submit an online application with the following documents:

1. Cover letter indicating the position level and research area applied for;

2. Curriculum vitae;

3. Research statement and plan, maximum 3 pages;

4. Teaching philosophy statement and teaching plan, maximum 2 pages;

5. Selected publications or major research outputs; and

6. Names and contact information of three referees

Nominations can be sent directly to the School of Energy and Environment (email: [email protected]).  Review of applications will start from 7 November 2026, and will continue until the post is filled.  Only shortlisted applicants will be contacted; and those shortlisted for the post of Assistant Professor will be requested to arrange for at least 2 reference reports sent directly by the referees to the Department, specifying the position applied for.  The University's privacy policy is available on the homepage.

City University of Hong Kong is an equal opportunity employer. We are committed to the principle of diversity. Personal data provided by applicants will be used for recruitment and other employment-related purposes.

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