Research Assistant

ETH SINGAPORE SEC LTD.; Singapore; On-site; SGD 40,000 - 60,000; Full time

Job summary

ETH SINGAPORE SEC LTD. invites a Research Assistant to join a multidisciplinary project on learning-based urban analytics and cognition. You will help operationalise evidence-driven decision support for high-density environments, integrating ML methods with field surveys and lab studies.

Under supervision of senior researchers, you will contribute to deep learning developments for geospatial data, participate in team meetings, and support academic outputs through writing and presentations.

Qualifications

• Master's degree completed and conferred.
• Experience with machine learning tools and workflows (e.g., Hugging Face, Keras, Torch).
• Experience deploying field data collection (online surveys).
• Research experience in end-to-end experiments and academic writing.
• Proficiency in Python and R; using git and AWS.

Responsibilities

• Contribute to multidisciplinary research to operationalise human-centred urban analytics.
• Develop and implement deep learning models for geo-spatial data and street-view analysis.
• Assist in data collection, literature reviews, and preparation of academic outputs.

Job description

Project background

The \"Mind the density - Quantifying design scenarios that soften high-density living\" project is a multi institutional and multi disciplinary project with researchers from the Singapore-ETH Centre, National University of Singapore, and the Singapore Management University.

Previous projects at the Singapore-ETH Centre targeted the lack of data convergence in urban planning and the application of the latest developments in geospatial data science to urban design and planning. Data commonly reside in silos (across different fields, agencies, and stakeholders), and analytical and AI methods do not fully exploit these multimodal datasets. Their research outputs tackled this problem directly by releasing open, converged datasets and developing methods that leverage different dimensions such as text, subjective indicators and imagery. In addition, work on design data semantics enhanced the meaning of data science applications for particular urban design solutions and actions. Moreover, they also investigated how the built environment affects people's behaviour, cognition, perception and emotion. They focused on the human and building scale, examining how indoor and outdoor environments influence experience.

This project integrates these expertise to operationalise evidence on human responses to high-density environments in Singapore. The project will generate large-scale preference data across controlled design conditions, and will conduct focused empirical studies of behavioural, cognitive, physiological and subjective responses. This combined evidence will support agency decision-making on design interventions that improve experience in high-density environments, identify thresholds, compare interventions, and produce decision-support evidence, planning parameters and implementation guidance.

Lead PI: Filip Biljecki (NUS) and Zdravko Trivic (NUS)

Job description

As a research assistant, you will be involved in a multidisciplinary and collaborative research study to operationalise human-centred urban analytics and cognition methods as evidence-backed decision support and implementation guidance for high-density urban form and design interventions. You will integrate analytical methods to quantify how urban form and design interventions affect perception, cognition and behaviour, producing URA-relevant guidance for high-density environments. You will also be involved in large scale field data collection through online surveys and lab-based physiological studies, and collaborations with researchers from various disciplines and government agencies stakeholders. You will do this under the guidance of Assoc. Prof. Filip Biljecki (National University of Singapore) and Asst. Prof. Matias Quintana (Singapore Management University).

• Co-developing the research of work package 1. This work includes, a.o.: Developing deep learning models for the project's research (e.g., geo-spatial data science, street view imagery segmentations). Co-developing applications, features, datasets, and use cases on public datasets of street-view imagery and longitudinal cohort studies. Co-developing and publishing journal and/or conference articles. Presenting your research to international audiences and visitors.
• Supporting the module research efforts in general. This includes, a.o.: Participating in and contributing to team meetings. Assisting researchers across the project in their activities. Contributing to the preparation of academic papers and other forms of academic output. Presenting the project internally and externally, and preparing material for such presentations. Lead and/or assist other activities such as preparation of funding proposals.

Selection Criteria

You have:

• A Master's degree (completed and conferred)
• Demonstrable experience with machine learning tools and workflows (e.g., Hugging face, Keras, Torch, etc)
• Demonstrable experience deploying field data collection (e.g., online surveys)
• Research experience in conducting end-to-end experiments and academic writing.
• Experience writing Python programs, R scripts, and using developer tools (e.g., git, AWS, etc)

You are:

• Self-motivated and independent
• An experienced programmer, able to easily adopt new programming languages or software design methods for particular purposes.
• A good communicator:
• Proactive: you can signal issues, set and commit to targets
• Rational: you can clearly articulate ideas and thoughts
• Interpersonal: you are able to take and provide constructive feedback
• A professional working proficiency in English is required.
• A team player: collaborative and constructive, and always open to help team members
• Interested in topics related to the research project.
• Curious and passionate to explore new concepts, methods and technologies. You have the capability to identify and quickly learn the most suitable tools for research on the go.

Significantly Beneficial Attribute