We are actively recruiting for several research positions. Watch this page for current and forthcoming positions with CARES.

 

C4T is CARES’ main research project. We employ researchers from chemical engineering, biotechnology, chemistry, biochemistry, information engineering, materials science and metallurgy to work on lowering the carbon footprint of Singapore’s chemical industry.

CLIC is a collaborative research programme between the University of Cambridge and NTU focused on improving support for lifelong learning and cognitive agility. The programme launched on 1 October 2020 and is looking to employ researchers from the fields of neuroscience, psychology, education and other related fields.

HD4 is a new project that brings together epidemiologists, clinicians, scientists, engineers, and architects to investigate how individuals move in space and time in Singapore, their corresponding exposure to risk factors that intersect with urban environments, and how this influences their behaviour.

HYCOMBS is a new project part of CREATE’s investment in Decarbonisation. The multi-institute collaboration between Singapore, Japan, France, and Norway focuses on the combustion fundamentals of hydrogen, ammonia, their blends, and their blends with hydrocarbon fuels.

SM3 is a new project part of CREATE’s investment in Decarbonisation. The project will address systemic challenges, including the integration of regionally available resources and the development of scalable, flexible technologies for local manufacturing.

 

 

     Job Vacancies

Centre for Carbon Reduction in Chemical Technologies (C4T)

1) Research Fellow (Mechanical/Chemical Engineer): We are looking to fill research positions in maritime decarbonisation modelling. The candidate will investigate topics such as CFD simulations of ammonia and methanol engines, and systems modelling of alternative fuels and application to real-world ship operation. The candidate will require a PhD or equivalent research experience in a combustion topic.

Centre for Lifelong Learning and Individualised Cognition (CLIC)

1) Research Engineer II (CLIC Graphic Designer): We are seeking a Research Engineer to design and develop captivating and motivational gamification elements for web-based cognitive tasks by working closely with the researchers. The candidate will manage and develop in-house IT, data management infrastructure (e.g. REDCap) and web-based solutions. Other tasks include providing server maintenance and technical support for research activities that range from specialist experimental equipment to in-house data processing and analysis pipelines.

Health-driven design for cities (HD4)

1) Transport Modeller (AI Researcher): We are seeking a Transport Modeller to work with the HD4 team of population health scientists, geospatial scientists, and data engineers. The post holder will lead on building an agent-based simulation model for Singapore. This role requires a multi-skilled individual who can work collaboratively in a multidisciplinary team environment.

2) Research Fellow (AI Researcher): We are seeking a Research Fellow to coordinate and report on work package activities, supervise other researchers, lead scientific publications, and collect open-source data to use in knowledge graphs towards a comprehensive and integrated city model of Singapore.

3) Research Associate (AI Researcher): We are seeking a Research Associate to compute urban heat island intensity, human biometeorology indices (PET, UTCI), and humid-heat stress; assess heat health risks and climate adaptation in vulnerable populations, evaluate social impacts of climate change on health and mobility, and develop a novel humid-heat index using multi-modal data for academic publication.

Hydrogen and Ammonia Combustion in Singapore (HYCOMBS)

1) Research Fellow (Mechanical/Chemical Engineer): We are seeking several Research Fellows for HYCOMBS. The candidate will require a PhD or equivalent research experience in a combustion topic relevant to (1) kinetics, (2) laminar and turbulent flame fundamentals, (3) high-pressure combustion, (4) detonations and ignition risk analysis with CFD.

Sustainable Manufacture of Molecules and Materials in Singapore (SM3)

1) Research Fellow (Chemical Engineer): The candidate should have a PhD in Chemistry or Chemical Engineering with a strong interest in Machine Learning (AI) for Chemistry and Chemoinformatics. The preferred candidate would have experience working with chemical databases, developing machine learning workflows based on chemical data for synthesis or properties prediction, and coding in Python.

2) Research Fellow (Chemical Engineer): The candidate should have experience in techno-economics and object-oriented programming, assessing engineering reports and scientific papers on novel synthesis technologies, and extracting key process performance parameters. The preferred candidate would have experience designing and performing simple experimental work in the broad area of process and reaction engineering.

3) Research Fellow (Chemist): Prof Matthew Gaunt (University of Cambridge) is seeking a Research Fellow to be based in CARES. The candidate should have a PhD in Organic Chemistry and a strong background in synthetic organic chemistry. The candidate will design and develop catalytic activation modes that lead to new synthetic transformations.

4) Research Fellow (Data Scientist): The candidate should have a PhD in a relevant field of Computer Science / Chemical Engineering / Chemistry with a combination of strong ML/AI expertise with chemical knowledge. The preferred candidate would be skilled in Python programming, experienced with LLMs and chemical reaction databases, and capable of handling reaction data analysis.

5) Research Fellow (Chemical Engineer): The candidate should have a PhD in Chemistry or Chemical Engineering with a strong interest in Process Systems Engineering and Machine Learning (AI) for Chemical Engineering. The preferred candidate would have experience developing first principle process models in Matlab/gPROMS or Python, process optimisation, and machine learning for chemical processes.