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will be given priority. Application To apply for this position please follow the link "Apply for this job". Your application letter, relevant education and work experience as well as language skills must
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Postdoctoral Fellow - Materials Chemistry, Texas Materials Institute, Cockrell School of Engineering
, click the Career link in the left-hand navigation menu, and then update the sections in your Professional Profile. This information will be pulled into your application. The application is one page, and
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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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applications. The role will focus on creating demonstrator platforms that combine sensing, data integration, analytics, and visualisation to support better understanding and management of energy use, flexibility
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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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@TorontoMet (external link) and @ECItorontomet (external link) on Twitter, and visit our LinkedIn company page (external link) . The opportunity The Robotics, Mechatronics and Automation Laboratory (RMAL
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integration of high-dimensional multi-omics, biomarker, and wearable-derived data. Aligned with the mission of the Charles Perkins Centre—to ease the burden of chronic lifestyle diseases and improve global
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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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assessment of and reflection on the use of AI agents in science. About you – for a PDRA candidate The successful applicant will be able to present information on research progress and outcomes, communicate
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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