Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
fingertips by enhancing the use of effective applications and services for teaching and learning. We drive a culture that is forward-looking. With a strong passion for IT, our people are always striving
-
Materials Generative Design and Validation Framework. The role will work at the intersection of machine learning, high-throughput experimentation, and materials discovery, focusing on accelerating the design
-
multiple Asian cohorts. The position focuses on data harmonisation, statistical genetics, and developing and validating machine learning cancer risk models. Key Responsibilities: Data harmonization
-
productivity through automation and technology enablement Leverage data and emerging technology (e.g., Power BI, Robotic Process Automation (RPA), Artificial Intelligence/Machine Learning (AI/ML)) to solve
-
, leveraging advanced learning analytics, machine learning, and deep learning techniques. The candidate shall work under the supervision of the Principal Investigator (PI) and Co-PIs to conduct academic research
-
The applicant should: have a Bachelor degree, preferably in Biomedical or related field; be able to work independently and in a team, be able to pay attention to details and learn new skills, have good
-
at its fingertips by enhancing the use of effective applications and services for teaching and learning. We drive a culture that is forward-looking. With a strong passion for IT, our people are always
-
learning and improvement through structured post-implementation reviews, driving better future project outcomes. ITSM Governance Support the ITSM lead in achieving annual ISO 20000 certification. Develop and
-
), hospitality and auxiliary services (car parks and shuttle bus services) in NTU main campus and NTU@one-north. OCAS is looking for a dynamic and experienced professional to be part of the team to manage the
-
Linux, with hands-on experience in data wrangling, pipeline development, and statistical analysis. Skilled in Machine Learning, including supervised and unsupervised methods for biological data