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omics data, digital pathology, using cutting-edge AI and machine learning approaches. You will play a critical role in developing sophisticated computational pipelines, integrating complex multi-omics
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tools, collaboration with project stakeholders, and engagement with the consortium and Defence and Security stakeholders. Technical Requirements: Strong coding skills with background in machine learning
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may also explore embedding these new computational methods into optimisation and machine learning contexts. The new computational techniques developed will be geared towards the following key
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. Fe, S) on CNT purity and structure. Evaluate CNTs as conductive additives in standard Li-ion battery electrodes. Apply AI/machine learning to optimise experimental design and growth parameters
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relevant professional certifications would be advantageous. 10+ years of working experience, including at least 5 in Artificial Intelligence, Machine Learning, Analytics, or a related area.SIT Internal
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role in the development of testing rig setups and the modification/re-purposing of existing rigs and equipment. Common activities include machining, cutting, water-jetting, welding, assembling and
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at leading international conferences and publish in top-tier journals. The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning
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rig setups and the modification/re-purposing of existing rigs and equipment. Common activities include machining, cutting, water-jetting, welding, assembling and working with cabling and hydraulics
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teach at the University of Vienna, where more than 7,500 academics thrive on curiosity in continuous exploration and help us better understand our world. Does this sound like you? Then join our
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an exciting opportunity to a graduate with the relevant skills and knowledge to develop and implement machine learning techniques to derive new-to-sector advanced insights from Aids to Navigation maintenance