1,865 data-"https:"-"https:"-"https:"-"https:"-"Linköping-University"-"IFM" Fellowship positions
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area with the potential to drive significant improvements in health and healthcare. You will be based within the Leeds Institute for Data Analytics and part of the Leeds Institute for Cardiovascular and
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the research team, by contributing to multiple research projects, including: • writing for ethics applications. • developing data collection tools. • recruitment of participants into studies and
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(SingFarm)—a CREATE-NRF programme focused on building a sustainable, data-driven food production model with strong industry relevance to contribute Singapore’s ambition in achieving 20 per cent local
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volatile organic compound (VOC) exposure across diverse real-world indoor environments. This role offers the opportunity to conduct mobile sensor measurements, analyze air quality data, and generate insights
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experience. They should also have a strong publication record in laboratory-based research and clinical data analysis. Applicants are invited to contact Prof. Chien-Ling Huang at telephone number 3400 8602
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for the Senior Research Fellow post should have a PhD degree in Computer Science, Artificial Intelligence, Data Science, Digital Economy or a related field plus at least six years of postdoctoral research
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project - “Automated monitoring traffic data system for detector status and non-recurrent events”. Qualifications Applicants should have: (a) a doctoral degree or an equivalent qualification and must have
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Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. NTU’s College of Computing and Data Science (CCDS) is a
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to the project, including setting up, organising and conducting quantitative interviews, quantitative and qualitative data analysis, preparation of draft funding applications, contributing to the authorship
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during