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pipelines. Your primary focus (80% of your time) will be on leading spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical
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technological solutions. DTU Health Tech’s expertise can be described through five overall research areas: Diagnostic Imaging, Digital Health, Personalised Therapy, Precision Diagnostics, and Sensory and Neural
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synergy with another postdoc working on the same project, whose focus is on the development, demonstration and application of the functionalized quartz resonators integrated into a sensor prototype. If
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industrial processes. Your research will drive a paradigm shift in how TES systems are modelled, integrated, and controlled within industrial settings. You will develop novel, adaptive, physics-informed models
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technological solutions. DTU Health Tech’s expertise can be described through five overall research areas: Diagnostic Imaging, Digital Health, Personalised Therapy, Precision Diagnostics, and Sensory and Neural
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
project aims to develop a field-realistic VLE prototype for education and training purposes of emergency response, construction safety and health, lean construction, construction progress inspection, and
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metabolism strategies to improve the capacity of these microbes for efficient, high-yield bioconversions from CO₂ and P2X feedstocks, such as methanol, formate, and hydrogen. Our ultimate goal is to prototype
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. Research Project & Objectives This PhD project aims to develop a field-realistic VLE prototype for education and training purposes of emergency response, construction safety and health, lean construction
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complete picture of fish habitat use and connectivity. The PhD is part of the section for Ecosystem based Marine Management and the Marine Habitats research group, as well as several synergistic initiatives
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paradigm shift in development of Biologics. A critical challenge preventing this is the current limiting volume of high quality and well-characterized in-vitro T cell immunogenicity data. In this project, we