72 machine-learning Fellowship research jobs at Nanyang Technological University in Singapore
Sort by
Refine Your Search
-
into operational use cases. Prepare data collection frameworks and work on fish health monitoring datasets for machine learning training and benchmarking. Support the development of translational “lab-on-farm
-
optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
-
into products and services for Continental through close collaboration with its business units. Key Responsibilities: To independently undertake research in artificial intelligence, machine learning system, edge
-
advances the mathematical foundations, algorithms, and real-world applications of epistemic uncertainty in machine learning, with a strong focus on imprecise probabilities, uncertainty representation and
-
Responsibilities: To perform pioneer research in scent digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different
-
leadership and expertise in the synthesis and characterization of advanced nanomaterials, specifically focusing on the integration of machine learning, wafer-scale synthesis of materials, and high-throughput
-
researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
-
, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
-
, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
-
Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes