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will play a key role in automated wildlife identification and classification from trap camera images using cutting-edge computer vision technology. Working closely with the Principal Investigator, Co-PI
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detection of microbial bioburden in biopharmaceutical cleaning validation processes. The role involves aptamer discovery, nanomaterial-based SERS sensor development, and integration with microfluidic systems
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AI applications as well as Python-based coding . Have a degree in Computer Science/Computer Engineering. Possessing a Master’s or PhD degree will definitely be advantageous. Knowledge of machine
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variants, EfficientNet, ResNet, U-Net) Image processing and computer vision techniques Python programming and relevant libraries (e.g., OpenCV, NumPy, scikit-learn, Pandas, Matplotlib) Experience with
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will play a key role in automated wildlife identification and classification from trap camera images using cutting-edge computer vision technology. Working closely with the Principal Investigator, Co-PI
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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welcome to apply. Familiarity with image data analysis tools such as SPM, FSL, AFNI, and/or DSIStudio etc will be an advantage. Signal processing and programming skills (e.g. with Matlab, Python) will be
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and