122 machine-learning-"https:"-"https:"-"https:" positions at SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
Sort by
Refine Your Search
-
degree will definitely be advantageous. Knowledge of machine learning, pytorch, huggingface etc... Knowledge of image processing is required. Ability to effectively and efficiently utilise industry
-
Singapore Institute of Technology (SIT) invites applications for Academic Staff positions in Cybersecurity. We are building a modern cybersecurity capability that brings together applied learning
-
of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical systems. Key Responsibilities Derive and analyse closed-form mathematical
-
of cybersecurity, engineering systems, critical infrastructure, and applied learning. We welcome outstanding candidates who can help SIT strengthen its capability in OT cybersecurity, especially colleagues who can
-
computer vision and vision-language models Experience with ML evaluation metrics and benchmarking Proficiency in Python and deep learning frameworks (e.g., PyTorch) Interest in applied, industry
-
foundational knowledge in signal processing and machine learning. Working knowledge of computer vision and deep learning concepts, including object detection and image-based classification, with hands
-
applications for Academic Staff positions in Cybersecurity. We are building a modern cybersecurity capability that brings together applied learning, competency-based education, translational research, and close
-
, microbial cultures, and cleaning validation samples. Develop data analysis pipelines for Raman spectral classification, potentially integrating machine learning methods. Research & Project Responsibilities
-
Engineering, Computer Science, Data Science, Statistics, or equivalent. Strong theoretical background in statistics and machine learning. Knowledge of the basics of federated learning and causal inference is
-
Operational Technology (OT) Cybersecurity. We are building a distinctive capability at the intersection of cybersecurity, engineering systems, critical infrastructure, and applied learning. We welcome