Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
-
Current federated learning architectures in mobile healthcare are limited to a centralised model without considering the full continuum of mobile-edge-cloud. Additionally, to support different data
-
methods dealing with model complexity - e.g., AIC, BIC, MDL, MML - can enhance deep learning. References: D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008
-
Machine Learning without Centralized Training Data”, https://ai.googleblog.com/2017/04/federated-learning-collaborative.html [2] “Learning with Privacy at Scale”, https://machinelearning.apple.com/research
-
at developing methodological contributions at the intersection of computer vision, multimodal learning, predictive world models, embodied AI, and human-robot interaction. The candidate will work towards models
-
[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
knowledge Artificial Intelligence; Machine Learning; Computer Vision; Bioinformatics; Biomedical Engineering; Neuroscience; Genomics; Medical Physics; Data Science; Biotechnology Project funding Project based
-
PhD candidate will have: A strong background in computer science, artificial intelligence, machine learning, or a closely related field. A solid understanding of machine learning concepts, particularly
-
encoded in computer software and can be used as decision support systems (DSS). These may be used by decision-makers with different domains of expertise than the analysts who built the DSS system. Therefore
-
publications in established machine learning, computer vision, artificial intelligence, and robotics venues. Depending on the contribution and maturity of the work, relevant conferences include NeurIPS, CVPR
-
, data processing pipelines, machine learning and generative AI applied to physical activity and sleep research. The position will contribute to the development of an AI-based behaviour change tool and