104 machine-learning-"https:"-"https:"-"https:"-"https:" positions at SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
As a University of Applied Learning, SIT collaborates closely with industry partners in our research pursuits. Our research staff can gain practical research skills that are directly relevant to
-
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
-
libraries such as HuggingFace Transformers, PyTorch/TensorFlow, and scikit-learn. Prior experience or coursework in natural language processing, machine learning, or information retrieval. Familiarity with
-
As a University of Applied Learning, SIT works closely with industry in our research pursuits. Future ship and system design (FSSD) program develop strategic and innovative design capabilities
-
, microbial cultures, and cleaning validation samples. Develop data analysis pipelines for Raman spectral classification, potentially integrating machine learning methods. Research & Project Responsibilities
-
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
-
conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities
-
As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skills
-
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
-
, accumulated deformations, and their impact on structural performance, particularly for compression members. Develop data-driven reusability assessment platforms integrating NDT data, machine learning models