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Field
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Systems, or a related field. Strong analytical and critical thinking skills. Strong machine learning (ML), computer vision (CV), large language models (LLM) for quantitative data, texts, images, and sensor
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, develop novel approaches for proving hardness, and devise new algorithms. You will be expected to conduct individual research and develop research questions, regularly write research articles at a national
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theoretical research, algorithm design, and the development of software tools that demonstrate the applicability of the new methods. Research environment The positions are hosted by the Department
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working with industry partners to translate this new technology into commercial sensors for vineyard installation. Further opportunities involve integrating the research data into the National Smoke
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for long-term monitoring to confirm successful restoration5. This exciting and timely PhD will develop and deploy a range of low-cost and open-source sensors to measure the biophysical properties necessary
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sensors, data acquisition, and statistical analysis. Ensure a safe and engaging lab environment. Collaborate with faculty to maintain consistency with course objectives and materials (already developed
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submission to journals and conferences. Present research findings at seminars, conferences, and scientific venues. Develop innovative and robust machine learning algorithms to personalize interventions
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, functioning as the technical expert, in the areas of vehicle safety and telematic technologies, general areas of control, hybrid state systems, simulation, sensing, estimation, sensor fusion, and telematics and
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Research Associate (Computer Engineering/Computer Science/Engineering/Mathematics/Biology/Chemistry)
. Key Responsibilities: Develop deep learning models and algorithms for biomedical and healthcare applications. Data processing for training of deep learning models. Mentor junior research lab members in
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, model compliance, and integration with operational systems. You will support digital project delivery and the University's Smart Campus initiatives, integrating spatial and sensor data to enhance building