Research Fellow, Pathology
- Employer
- NATIONAL UNIVERSITY OF SINGAPORE
- Location
- Singapore
- Closing date
- 26 May 2021
View more
- Academic Discipline
- Biological Sciences, Computer Science, Engineering & Technology, Clinical, Pre-clinical & Health, Life sciences, Medicine & Dentistry, Electrical & Electronic Engineering
- Job Type
- Academic Posts, Research Fellowships
- Contract Type
- Permanent
- Hours
- Full Time
Job Description
This position reports to Principal Investigator A/Prof Tan Soo Yong and interfaces with pathologists, scientists and industrial collaborators. A/Prof Tan’s research team in the Department of Pathology, National University of Singapore, currently works with a local start-up company MiRXES to develop AI-enabled molecular diagnostic/prognostic assay for Diffuse Large B-cell Lymphoma (DLBCL) and other cancer subtypes. The team also works with Qritive, a local start-up company that provides artificial intelligence (AI)-powered solution to help doctors analyse microscope images and make disease diagnosis.
Exposure to the following Functional Areas:
- Development of workflow for AI-enabled molecular diagnostic and prognostic modalities
- Pathological laboratory skills such as sectioning and processing of formalin-fixed paraffin-embedded (FFPE) patient biopsies and immunohistochemical staining
- Research regulatory administration such as application for use of human tissue biopsies via DSRB, clearance of laboratory safety requirements via OSHE
- Research communications / dissemination through lab meetings, journal clubs and grants/manuscript preparation
Specific Responsibilities:
- Perform bioinformatic analysis and AI-enabled molecular diagnosis using microRNA expression profiling of DLBCL and other cancer subtypes (in collaboration with MiRXES Pte Ltd)
- Assist in clinical validation of image analysis tools to enable AI-enabled clinical diagnosis (in collaboration with Qritive Pte Ltd)
- Perform experiments relating to analysis of processed FFPE patient biopsies for development of AI-enabled molecular and image-based diagnostic modality
- Assist PI in preparation of documents for submission to research regulatory board
- Assist PI in organising lab meetings and journal clubs
Competencies that PDF will learn during fellowship stint:
- Active participation in study conceptualisation, experiment design and development of workflow for AI applications
- Transferable skillsets such as time management, teamwork, personal motivation, written and oral communication skills and leadership
- Networking with both basic and clinical science researchers and -industrial partners overseeing bench-to-bedside product translation for clinical benefits
- Experience in managing research regulatory requirements
Qualifications:
- PhD in Life Science / Biomedical Science / Biology / Biotechnology / Bioinformatics / related fields, or PhD in Computer Science / Electrical & Computer Engineering / Computer Programming / related fields ;
- Effective oral and written communication skills ;
- Good team player ;
- Strong research skills.
Proposed Development Plan
- Technical Skills and Competencies Development of workflow to enable deep learning for AI-enabled molecular diagnostic/prognostic clinical modalities suing microRNA expression profiles, involving industrial partner MiRXES
- Outcomes To gain competency in development of workflow for AI -enabled molecular diagnosis/prognosis + Industrial experience (MiRXES)
- Training Duration 18 months
- Technical Skills and Competencies Pathological Laboratory Skills (microtome sectioning, section processing, immunohistochemical staining)
- Outcomes To gain competency in performing independent experiments involving these histopathological skills
- Training Duration 2 months
- Technical Skills and Competencies Preparation of documents for submission to NUHS research regulatory board such as OSHE and DSRB
- Outcomes To gain competency in assisting PI in preparation of duly completed application dossier for successful submission
- Training Duration 1 month
- Technical Skills and Competencies Involvement in clinical validation of image analysis tools to enable AI-enabled clinical diagnosis (in collaboration with Qritive Pte Ltd)
- Outcomes Develop workflow for management of large batch clinical validation + Industrial experience (Qritive)
- Training Duration 3 months
Application Procedure: Please apply with your CV and contact details of 3 referees
Only shortlisted candidates will be notified.
More Information
Location: Kent Ridge Campus
Organization: Yong Loo Lin School of Medicine
Department Pathology
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