Job Description

Duties and responsibilities

The Urban Analytics Lab at the National University of Singapore (NUS) is seeking a research assistant to join the research group and assist with research in the geospatial & data science domain – 3D city models / BIM, data integration (focusing on crowdsourced and dynamic geospatial data) and use cases, under the umbrella of digital twins.

The successful applicant will be engaged on novel research lines in the context of digital twins, namely: integration of nascent data sources, initiating new means of benchmarking and quality assessment of data and workflows, and developing new use cases pertaining to building/urban/geospatial data infrastructure in the frame of smart cities.

Candidates should have a computational affinity and a degree in computer science, geographical information science, geomatics, mechanical engineering, or another relevant field of study, with a demonstrated track record (in terms of working experience and/or publications/impact and/or other forms of output such as development of tools).

Qualifications

Qualifications & Skills Requirement

Required qualifications

You should demonstrate that you possess:

• A degree in a related discipline from a reputable university. Current students are eligible to apply as well, provided that they are in the final stage of their studies and that their degree will be completed before the start of the appointment.
• An ability to conduct research semi-independently.
• A publication record is not required, but candidates with demonstrated impact, writing skills, and a strong affinity to publish are welcome to emphasise these in their application.
• Proficiency in one or more of the following areas: GIS, computer science, game engines, Geomatics, 3D city modelling, computer vision / AI / ML, urban planning, and/or urban simulations.
• Good communication skills in English, and an ability to present research in both academic and non-academic venues.
• Experience with programming. We will prioritise applicants who can demonstrate these qualities through a Github profile or similar evidence.
• Curiosity and passion to explore new concepts, methods and technologies, and capability of identifying and quickly learning the most suitable tools for the research on the go.
• A positive and proactive stance on open science, preferably with demonstrated activities such as using and developing open-source software, releasing data as open data, and promoting reproducible research.

Our offer

• You will be working on compelling research activities at the forefront of geographical information science and urban data science in a new research group that is gaining a strong momentum in Singapore and overseas.
• You will advance your research and transferable skills required for the next stage of your career. You will be welcome to explore and learn new tools and technologies, and expand your current skillset.
• You will work in a research and educational environment where you may not only contribute to your own development, but also to others, e.g. by supervising master theses that may arise as part of the research.
• Opportunity to make impactful contributions in the field, on a globally relevant and growing topic.
• Visibility and exposure of your work.
• Potential contact with industrial and governmental partners, both locally and internationally.
• A diverse, inclusive, friendly, and flexible working environment.
• Exploring new ideas and an opportunity to experiment with the state of the art.
• The remuneration will be determined according to the university’s salary range depending on the degree and experience.

Please note that this is not a PhD position. This is a full-time research job.

Application

Please apply by email to filip@nus.edu.sg (please write “Application for the postdoc position on digital twins” in the subject). The full text of this ad and the list of required materials is available on the Lab’s website (https://ual.sg).

More Information

Location: Kent Ridge Campus
Organization: School of Design And Environment
Department: Architecture
Employee Referral Eligible: No
Job requisition ID: 8394