Research Associate (Programmer for Adaptive Learning)

The successful candidates will contribute to research activities related to evaluation and development of an online adaptive learning system for teaching project management skills to construction professionals. The candidates shall work under the supervision of the Principal Investigator (PI) to conduct programming, academic research and administrative work. Specific job activities may include:

•    Conduct literature review on topics related to the project;
•    Develop the following functions of the adaptive learning system:
•    Knowledge unit map
•    Knowledge unit recommender
•    Instructor dashboard
•    Adaptive quiz
•    Forum recommender
•    Liaise with collaborators and users on testing, meetings and data collections;
•    Write research papers, reports and proposals;
•    And any other tasks required by the principal investigator.

•    A Masters within the fields of computer science, software engineering, or related field
•    At least 2 years’ relevant work experience, and good knowledge, skills and expertise in relevant field.
•    Experience in any of the general purpose programming languages, such as Java, Go, C++, C#, python. Preferably python;
•    Experience in web development framework like Flask or Django;
•    Familiar with SQL;
•    Experience in designing and building REST APIs;
•    Knowledge of HTML, CSS and Javascript;
•    Willing to learn, independent, team player, professional, proactive, communicative, and responsible; and
•    Strong English writing and communication skills.
Candidates with the following will be preferred (not compulsory):
•    Familiar with at least one of the deep learning frameworks like Tensorflow / PyTorch.
•    Understanding of NLP.
•    Good understanding of graph data structure  
(* Interested applicants, please indicate current and expected salary in your job application/ CV.)

Location: Kent Ridge Campus

Organization: College of Design and Engineering

Department : The Built Environment

Employee Referral Eligible: No

Job requisition ID : 19626