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Field
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passion for applying advanced machine learning to real-world medical challenges. If you thrive in collaborative, multi-disciplinary environments and possess a strong technical foundation in AI methodologies
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qualification; (ii) demonstrate research skills in imaging science and machine learning, particularly on image reverse engineering, fake image and video detection, statistical detection models and mathematical
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areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and Multimodal Models Generative AI, Agentic AI, Physical AI, and
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with programming in Python is a requirement Experience with telecentric particle imagers, image analysis, and machine learning for particle recognition is an advantage Experience of working with wave
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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
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requirement Experience with telecentric particle imagers, image analysis, and machine learning for particle recognition is an advantage Experience of working with wave flumes to study entrainment is an
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will entail movement among the open office space, the galleries, and the campus and community, along with use of computer and audio/visual equipment and the lifting of materials of approximately 35
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Radiology faculty collaborator, to define and address clinically meaningful research problems. • Design, implement, and evaluate machine learning and AI methods for medical imaging using real-world clinical
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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
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looking for postdoctoral researchers in the area of computer vision, AI, and machine learning. The initial appointment will be for 2 years with a possible extension with a tentative start date in January