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Field
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approaches for binarized network models, identifying their strengths, limitations, and applicability within privacy-focused machine learning frameworks. Special attention will be given to evaluating
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and disease, through accurate machine learning models. Current areas of interest include developing deep learning approaches for genome interpretation; development of methods for multi-omic and
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RE-C05-i08 do Programa de Recuperação e Resiliência, através da Fundação para a Ciência e a Tecnologia - FCT, nas seguintes condições: Scientific Area: Computer Engineering, Biomedical Engineering
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to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig
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, analysis and evaluation, and writing reports which contain descriptive, analytical and evaluative content. The purpose of this role is to acquire the professional skills needed to pursue a career path of his
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of 17% superannuation applies. Research Fellow in machine-learning enabled digital forensics Fixed term, full-time 36-month position available About the position We are seeking a Research Fellow with
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the last three years. Solid experience with AI/machine learning methodologies, particularly those applicable to network optimization. Proven ability in programming and familiarity with network simulation
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of Birmingham is inviting applications for a Research Fellow position focused on Machine Learning for Automated Formal Verification. Machine learning has transformed programming, with code generation rapidly
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health records (EHR), waveforms from bedside monitors, radiology images and wearable sensors. This position offers a unique opportunity to work closely with clinicians on applications of machine learning
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to facilitate the integration of the framework with external systems and educational platforms; Establish a Machine Learning Operations (MLOps) pipeline to automate the lifecycle of models, including training