SG Enable – Intern, Innovation

About the Division

Innovation division establishes unique enterprises and capabilities to serve the disability sector. It also works with other divisions to align unmet needs and manage projects that attain scale of operations. A new and additional focus of the division will be to leverage existing, well-curated public disability databases to analyze distinct or cross-cutting domains and questionnaires that allude to program-planning and resourcing.

Project Description

There are many underpinning functional and biological differences that can contribute to selected profiles of Persons-with-Disabilities (PwD). Current methods to categorize PwDs for subsequent training and employment may be confounded by a lack of granularity in describing clinical phenotypes. We propose a study where machine learning and statistical analyses are applied to advanced international public datasets in countries where inclusiveness is a key component of societal norms. Our goal is to evaluate profiles that can be adopted in our existing frameworks, inform predictive outcomes validated in advanced, independent and publicly available PwD cohorts. The successful candidate will attend public seminars at universities with the supervisor for exposure to the research community. He/she will also attend the supervisor's external lab or group meetings for increased interactions with the research community. All discussions with the candidate will be based on public datasets.

Key Duties and Responsibilities

The undergraduate full-time student intern will be enrolled through universities in Singapore that have supercomputing infrastructure. A full faculty advisor will co-supervise the student (to be decided by SG Enable supervisor, Carol Tang). This partnership ensures that the project will be executed in a scientifically robust manner with conclusive outcomes. The skills acquired will entail developing machine learning algorithms to query parameters that can inform categorization of PwDs for more targeted training and/or employment. These bioinformatical skills are not entirely novel as they are already applied in different context to other sectors; thus, the student will learn the essential thought process in function-driven evaluation.

Requirements

• Good spoken and written English.
• Analytical, scientific mindset.
• Open to new technologies and analytical tools.
• Proficiency in specialized software is an asset but not compulsory (e.g. Clarivate patent-mining, R programming).

The interview process will encompass live evaluation of case studies.