Research Assistant (Geospatial analytics and urban data science)

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Assistant-%28Geospatial-analytics-and-urban-data-science%29/32659-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

We are seeking a research assistant to assist with a project and research in the geospatial & data science domain.

The primary tasks are:
. Conducting research activities.
. Literature reviews.
. Project management and administration.
. Development of software tools and related solutions.
. Collaboration with other researchers and coordination, and co-authoring publications and other forms of output.
. Exploring new research directions and experimenting with new datasets and technologies.
. Assisting the PI in various activities in the research group and project such as supervision/mentorship of students, management, administration, and funding applications.
. Preparation of publications and managing other forms of research output such as software and data.

Qualifications

. A Master's degree in a related discipline (e.g. urban planning, geographic information science, computer science). Current master 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 independently.
. Expertise in statistics.
. Expertise in geospatial technologies and state of the art urban data such as street view imagery and place reviews.
. Experience in research, preferably with publications and other forms of relevant academic outputs.
. Excellent communication skills in English, writing skills, and an ability to present research in both academic and non-academic venues.
. Experience with programming (primarily Python), scraping, database management, and data science, machine learning and computer vision libraries.
. 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.