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approaches (based on functional programming abstractions) to optimize the implementation of machine learning models and other digital signal processing algorithms on a specific FPGA architecture to fit within
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a training dataset for developing machine learning algorithms for increasing the consistency of quality control in two cohort studies: healthy controls and epilepsy patients. Key Responsibilities
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developing image analysis and machine learning algorithms and tools for aerial imaging and analysis. You will also contribute to data collection, data curation, and the development of a data portal for project
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The fellow will be responsible for: Building collaborations with our multidisciplinary team (medical physicists, engineers, computer scientists, nuclear medicine physicians) to develop and implement innovative
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learning with advanced algorithms such as Alphafold3 for molecule processing and foundation models for image processing. Designs and develops machine learning computer models (i.e. algorithms) for medical
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scientists, nuclear medicine physicians) to develop and implement innovative AI algorithms applied to medical images To lead effort on enabling translational and physician-in-the-loop AI solutions for medical
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in image analysis. Team player. Education/Experience: Pursuing BSc in Biology or Computer Science. Experience coding in Python. Machine learning experience is a strong asset. Experience with image
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platforms • Digital signal processing • Electrical engineering • Instrumentation science • Manufacturing and machining • Mechanical engineering • Millimetre-wave technology • Optical
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coordination High-level computer skills and literacy, including knowledge of MS Office products, especially MS Teams, Word, PowerPoint, and Excel Experience designing in e-learning environments, utilizing
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projects focused on Generative AI and related technologies, such as machine learning and IoT, with a high emphasis on delivering sophisticated infrastructure-as-code prototypes using AWS Cloud. This role