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medical datasets, particularly images and texts, as well as tabular and higher-dimensional data Testing, implementation, and benchmarking of new research methods Development of machine learning approaches
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protocols to characterize both cellular and vascular properties of the TME. The approach will be validated using a combination of in silico models, computer simulations, and in vitro experiments using tumor
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the detector’s lines of response. The candidate will develop a hardware attenuation correction by generating attenuation maps from 3D models of RF coils created using computer-aided design (CAD), or from clinical
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engineering, and have expertise in building camera systems and computer vision. This role also involves working closely with the other members of the team on projects related to plant phenotyping (assessing e.g
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tasks and seminars, training of new group members in laboratory techniques) Independent work ethic and desire to learn new methods and protocols Basic R computer programming skills Basic software skills
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contribute to developing and/or applying artificial intelligence, machine learning, and image segmentation and/or data science-based methods in medical imaging processing. Salary Range: $47,000 - $70,500
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Research Infrastructure? No Offer Description The requested figure will be responsible for developing and implementing both machine-learning methods for analysing images and audio files in Python, as
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activities, including maintenance of imaging pipelines and computer equipment. Keeps accurate and detailed records of imaging dataset, pipelines and results. The incumbent will be able to perform routine image
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researchers, and students on advanced machine learning research and prototype deployment. Job Requirements Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or related discipline Strong
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requirement. A very good command of the English language, both written and spoken, is a key requirement. Experience in Federated Learning, Computer Vision, Image Analysis, Mathematics, and Mathematical