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classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence
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translation of innovative miniature, hair-thin imaging devices we have previously developed [https://doi.org/10.1117/1.JBO.29.2.026002]. These devices are designed to enable early and accurate detection
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recently developed in a commercial 65 nm CMOS imaging process by a large international consortium of engineers and scientists for the ALICE ITS3 upgrade and the future experiments, ePIC@EIC and ALICE3@LHC
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, operation of the scanner, data acquisition and analysis, image evaluation, and statistical analysis. The fellow will also be expected to prepare manuscripts and conference abstracts related to projects and
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Technological University, NTU, and National University of Singapore, NUS, and hosted by NTU. IDMxS is focused on development of core science to drive a paradigm shift in molecular detection and analysis to link
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and R scripts to automate complex and time-consuming tasks within ongoing research projects. Apply deep learning methods to enhance image processing tasks, ensuring cutting-edge solutions in medical
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areas. Key Responsibilities: To independently undertake research in computer vision and machine learning. To produce research reports and/or publications as required by the funding body
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will include: 1. Implementing and quality-assuring MR Fingerprinting scans on multiple clinical scanners 2. Integrating advanced imaging tools for image processing and analysis. 3. Coordinating closely
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written skills. Presentation skills. Analytical skills. Attention to detail. Responsibilities Research Perform patch clamp electrophysiology, calcium imaging, rodent survival surgery, and
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repository and computer servers. Run existing PET/MR brain image processing pipelines on the computer servers, produce the results, and communicate with the group members. Write computer codes for the above