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models based on survey and secondary data. Creative approaches to combining and leveraging various data sources are highly encouraged! The selected Ph.D. candidate will benefit from our outstanding
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. As part of this PhD, the candidate will: - Conduct an integrative review of established competency models - Create assessment tools (which may include use of AI tools) to measure CLMA proficiency
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explore how to combine data-driven and model-based approaches to assess the quality of returned parts, monitor in-process performance in real time, and support intelligent decisions on remanufacturing
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fluorescence microscopy to follow in real-time how nanomedicines engage with living cell membranes (and simpler model systems) and quantify these interactions. Similar studies can be performed for understanding
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preclinical in vivo animal models? Then this position might be perfect for you! Join us! We are looking for a highly qualified and motivated PhD candidate to conduct research activities aimed at unravelling
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application form including a research proposal (max 2000 words), merged in 1 document, motivation letter, CV (max 2 pages), copy Master’s diploma and transcripts The application form and more information can be
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-based predictive models to anticipate user engagement, primarily using data collected through unobtrusive measurements (e.g., websites, smartphones, smartwatches). The goal is to construct a AI-driven
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? To what extent and how do features of the collaborative process have to account for existing citizen initiatives? - How can we integrate models of social influence and “diffusion on networks” with models
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researchers working on innovative projects with as doctoral candidates and post-docs. It has been accommodated in three multidisciplinary UT research institutes: Mesa+ Institute, TechMed Centre and Digital
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. Copies of the master's diploma and a list of grades. A copy of one chapter of your master’s thesis, an article, a book chapter, or other academic text in English that you feel best represents your work