492 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at National University of Singapore
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
website content initiatives and strategies to drive traffic and engagement, including for LKYSPP’s thought leadership platform (http://global-is-asian.nus.edu.sg/) and its supporting social media channels
-
modalities of PET-CT and PET-MRI. For more details on what we do, do visit https://medicine.nus.edu.sg/circ/. Appointments will be made on a two-years contract basis with the possibility of extension
-
international law. More on CIL can be found here: https://cil.nus.edu.sg/ About the Oceans Law and Policy Team: Ocean Law & Policy is one of CIL’s core programme areas and was established over 10 years ago. It
-
insights from cutting-edge clinical and translational research. More information on the Department’s research portfolio may be obtained from https://medicine.nus.edu.sg/obgyn/. The Core Support Faculty will
-
role in designing composites materials using inorganic solid electrolytes using computational modelling and machine learning. Qualifications • Ph.D. in Materials Science, Chemistry, Physics, or a
-
and interpretable machine learning systems. The successful candidate will work on projects involving ensemble learning, large-scale data analytics, and high-performance model design, aimed at developing
-
Canvas LMS according to provided specifications and learning objectives. Perform quality checks on uploaded content to ensure accuracy, formatting consistency, and alignment with educational standards
-
and interpretable machine learning systems. The successful candidate will work on projects involving ensemble learning, large-scale data analytics, and high-performance model design, aimed at developing
-
or Teaching Assistant at the National University of Singapore (NUS) Are you passionate about data science, machine learning, and artificial intelligence? Do you enjoy teaching and empowering others with
-
enrichment (GO, KEGG), network analysis, genome assembly and binning, systems biology, and multi-omics integration. Apply statistical modelling, machine learning, and deep learning approaches for biomarker