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experience in one or more of the following AI sub-domains: machine learning, deep learning, natural language processing, generative AI, automation, computer vision, data (e.g., sensor) fusion, robotics, and
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join our team, focusing on the commercialization of renewable energy innovations as part of the Research Ireland project RENEW. This role combines technical expertise in system enhancement and machine
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of Artificial intelligence and Personalised Medicine. Candidates with expertise and a strong research interest in the following areas are highly encouraged to apply: Artificial intelligence, Machine learning
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to market. We are looking for a candidate with deep knowledge of natural language processing and machine learning who will demonstrate the requirements for the role as outlined below and must satisfy all
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Ireland project RENEW. This role combines technical expertise in system enhancement and machine learning with strategic business development skills to drive the adoption and market success of innovative
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to end” view of learning, keeping student experience firmly at heart. The successful candidate will be an experienced senior academic leader with a track record of successful academic planning and strategy
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condensed matter, and Computational modelling or you can apply for the specific project titled “Machine learning and Nanoparticle shape classification: using advanced descriptors”. The selected candidate will
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, parallel algorithms, Scientific computing and data processing, Cloud computing, Optimisation or you can apply for the specific project titled “Reducing Energy Cost of Communication of Machine Learning Based
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, architecture, machine learning, economics, social sciences and environmental sciences and construction industry stakeholders. Please submit the following documents as part of your application: A cover letter
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and suitably structured for the project needs. Data will also be used to develop machine learning models for the preventions and early detection of Cerebral palsy and the Maternal & Infant Clinical Data