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analysis of cell signalling, this three-years postdoc position at DTU Bioengineering will fulfil your scientific and personal development in an international, multi-disciplinary, and curiosity-driven
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performing educational research (mix-methods, surveys). Your primary tasks will be to: Implement full-stack applications, including an AI model in the back-end and GUI (in HTML); Deploy AI models both locally
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microbiomes” and “The best mix – mapping cultivar mixtures in wheat and interactions with the microbiome”. Your main tasks will be to characterize microbiomes associated with plant roots to elucidate
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microbiomes” and “The best mix – mapping cultivar mixtures in wheat and interactions with the microbiome”. Your main tasks will be to characterize microbiomes associated with plant roots to elucidate
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international, regional, and national routes to accountability and remedy for victims and the successful candidate will join the MERCURY team in applying a ‘mixing methods’ methodological approach where we employ
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evaluating student learning in STEM environments. The ideal candidate would demonstrate competence with a mix of methodologies, including primary data capture (eg interviews and surveys) as
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. Qualifications Applicants with experience in mixed methods, particularly digital methods, data scraping/analysis and qualitative research (Netnography and interviews) will be preferred. Proficiency in English and
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productively with both the CAISA research team and external organisations and institutions experience with mixed methods Furthermore, it will be considered an advantage if applicants can document: experience
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also include developing new controls for advanced Personalized Environmental Control Systems (PECS) including wearable heating and cooling systems. This will include identifying optimal input signals