Postdoctoral Fellow, Faculty of Dentistry
National University of Singapore
- Singapore
- Permanent
- Full-time
Accept ClosePress Tab to Move to Skip to Content LinkSearch JobsJob DescriptionJob Title: Postdoctoral Fellow, Faculty of DentistryPosting Start Date: 03/09/2025Job Description:Job DescriptionWe are looking for a dedicated and highly skilled Postdoctoral Fellow with a proven dual-mode research profile capable of independently performing laboratory experiments and coding predictive AI models in Python to forecast biomaterial behaviour for use in dentistry. This is not a data-science-only position; prior hands-on experience in the production, characterization techniques of materials, and cell culture is required.Core Responsibilities
- Develop and implement Python-based AI/ML models (e.g. LSTM or time-series models) to predict long-term properties of bioactive materials.
- Conduct independent testing on restorative and bioactive materials under simulated oral conditions.
- Characterize independently biomaterials composition, alkalinity, mechanical properties .
- Characterize independently biomaterials ' bioactivity using mammalian tissue culture assays to assess cell-material interactions.
- Integrate experimental and computational workflows; analyse, document, and report results.
- Prepare scientific manuscripts and grant proposals.
- A Bachelor's degree in Dentistry or Dental Technology is mandatory, providing a foundational understanding essential for biomaterial research in dental applications.
- A doctoral degree (PhD) in Dentistry, Biomedical Engineering, Microbiology, or a scientifically related discipline, underscoring expertise in both biological and engineering aspects of biomaterials.
- Demonstrated proficiency and autonomy in laboratory techniques for the characterization of dental biomaterials' physical, mechanical, and biological properties are essential to succeed in this role.
- Advanced skills in Python programming for machine learning and artificial intelligence, with a strong emphasis on developing and applying models such as LSTM and other time-series analyses to predict the longevity and behaviour of bioactive materials.
- Experience in mechanical testing methodologies, including wear assessment, fracture strength evaluation, and fatigue testing, alongside chemical analysis techniques like ion leaching and degradation studies.
- Expertise in mammalian cell culture and molecular biology techniques, including quantitative PCR (qPCR), Western blotting, cell proliferation assays, and mineralization assays, is required to investigate cell-material interactions comprehensively.
- Practical experience operating advanced microscopy equipment, such as scanning electron microscopes (SEM) and confocal microscopes, to support detailed material and cell analysis.
- A proven track record of publishing peer-reviewed research papers that bridge the domains of machine learning and biomaterials, demonstrating the candidate's ability to contribute original knowledge to the scientific community.