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devices, and edge computing platforms. Deploy machine learning models to enhance process control and system responsiveness. Collaborate with industry partners to identify research challenges, co-develop
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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, which means the thesis must be submitted by the role’s starting date) in an appropriate field (e.g. architecture, civil engineering, energy, energy in buildings, community energy). PhD equivalence is
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, software development, and research experiments. Contribute to the development of working prototypes and demonstrations for mobile biometric systems utilizing federated learning architectures. Prepare
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circuit modeling, simulation, and layout design techniques. Proficiency with using relevant tools such as ADS, HFSS, Cadence and Matlab etc. Familiarity with RF/mm-wave transceiver architectures and
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implementation Knowledge of networking protocols and architectures PhD or equivalent R&D experience in computer science, networks or communications, and/or experience with at least one of the following areas
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the architecture of multi-trophic ecological interaction networks to the impacts of invasive plant species on ecosystem multifunctionality to inform approaches for ecological restoration. The ideal candidate will
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, cloud computing, and distributed architectures, to enable efficient analysis of large-scale biomedical datasets. Collaborate with clinical and academic partners, both internally and externally, to ensure