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, protocols, and data standards across collaborating institutions and scales. This collaboration will support the generation of coherent, high-quality datasets and enable the development of predictive models
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optimization or related inverse design techniques. While this position does not involve developing AI models, it requires close collaboration with AI researchers to ensure data is appropriately structured for AI
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in topology optimization or related inverse design techniques. While this position does not involve developing AI models, it requires close collaboration with AI researchers to ensure data is
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] that process information in temporal rather than spatial modes to reduce their footprint. The project involves a collaboration between DTU Electro (Senior Researcher Mikkel Heuck) and Harvard University (Dr
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international quality, including publication. Single cell RNA sequencing and data analysis Characterization and production of monoclonal antibodies Flow cytometry Confocal microscopy qPCR In vitro cell cultures
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analytical chemistry and with a preference to a strong background in chemistry. Candidates with practical experience in non-target analysis and data analysis workflows, gas chromatography of very volatile
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empathic person with a PhD degree in mechatronics engineering, MEMS, neurotechnologies, applied physics, nanotechnology, neural interfaces, biomedical engineering or similar. Advanced skills in electronics
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Fermentation, and Data science to convert to maximize both data quality and quantity. You will join the DTU Characterization teams, which include a professor, an associate professor, a research assistant, and a