Research Assistant

ETH Singapore SEC Limited; Singapore; Hybrid; SGD 39,000 - 61,000; Full time

Benefits offered by this job

25 days annual leave for fixed-term

1 day Birthday Leave

Annual dental benefits

Healthcare insurance coverage

Flexible hybrid work (up to 2 days/wk)

NS mark certification

Job summary

ETH Singapore SEC Limited invites a motivated research assistant to join a multi-institutional project on high-density urban design. You will help integrate machine learning models with urban analytics and cognition methods, and contribute to evidence-based design guidance.

You will support data collection through online surveys and lab experiments, work with researchers from NUS and SMU, and contribute to scholarly publications under senior supervision.

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 (e.g., online surveys).
• Research experience with end-to-end experiments and academic writing.

Responsibilities

• Assist in multidisciplinary research integrating urban analytics and cognition methods.
• Quantify how urban form/design interventions affect perception and behaviour.
• Participate in large-scale field data collection via online surveys and lab studies.
• Contribute to academic papers and presentations.

Skills

Machine learning workflows

Python programming

R scripting

Git

AWS

Education

Master’s degree (completed and conferred)

Tools

Hugging Face

Keras

Torch

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)

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).

1. 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.

2. 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.

1. Proactive: you can signal issues, set and commit to targets
2. Rational: you can clearly articulate ideas and thoughts
3. 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 Attributes

• Detail-oriented and meticulous in your work.
• A developed GitHub repository that demonstrates your past or ongoing work, and skills.
• A strong interest in developing spatial data science and urban-related topics.

Desirable Attributes

• The ability to present your work and progress in an engaging and effective way.

Why SEC is your employer of choice?

• Accredited with 5 Tripartite Standards by Tripartite Alliance for Fair & Progressive Employment Practices (TAFEP) Singapore.
• A diverse workplace with 32 nationalities, offering ample opportunities for mutual learning.
• Positive and inclusive working environment.
• 25 days of annual leave for fixed-term contracts.
• 1 day of Birthday Leave.
• Annual dental benefits.
• Committed to being a supportive employer as you prioritize your physical and mental wellness.
• Comprehensive healthcare insurance coverage.
• Flexible hybrid work arrangement (up to 2 days per week from home).
• Abundant networking opportunities across various disciplines.
• Accredited with NS mark certification.

The Singapore-ETH Centre is an equal opportunity and family-friendly employer. All candidates will be evaluated on their merits and qualifications, without regards to gender, race, age or religion.

Curious? So are we.

Work location: 1 Create Way, CREATE Tower, Singapore 138602 (NUS University Town)

Duration: Full-Time position (12-months Contract)

Remuneration will be commensurate with the candidate's qualifications and relevant experience.

Further information about the Singapore-ETH Centre can be found on our website: https://sec.ethz.ch/

For further information, please contact: Filip Biljecki (NUS) at ***email_hidden***, (strictly no applications)