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the invasive comb jelly Mnemiopsis leidyi, one of the very few animals known to produce coelenterazine, as model organism in our laboratories at University of Copenhagen. Apart from studying bioluminescence in
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-driven algorithms Ability to conduct research independently while working effectively within a multidisciplinary academic–industrial team Strong written and spoken English, other language skill is a plus
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technologies through curiosity-driven research, and on mission-driven innovation involving successful co-maturation and integration with industry. Achieving this requires a deep understanding of the CCUS
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postdoctoral researcher to join an interdisciplinary team developing deep learning models for antimicrobial resistance (AMR) detection directly from MALDI-TOF mass spectrometry data. The project is funded
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Assistant Professor in statistics for the development of privacy-enhancing techniques in health care
substantial potential to improve diagnosis, prognosis, and treatment across a wide range of diseases through data-driven analytical approaches. Progress in developing robust methodological frameworks relies
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multi-fidelity modelling, your research will advance and integrate three core elements: (i) physics-based multi-fidelity structural models enabling high-resolution analysis at fatigue-prone hot-spots
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circuit models and algorithms for estimating the charge, health, and power based on direct methods (e.g. open circuit voltage), model-based methods (e.g. Kalman filtering), data driven methods (e.g. machine
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Two DTU Tenure Track Assistant Professors in Autonomous Modelling and in Robotic Synthesis of Ene...
, automated experimentation, advanced characterization, and data-driven decision making. We seek outstanding candidates in (i) AI-enhanced multiscale materials modelling and (ii) autonomous materials synthesis
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sensing, AI-based methods and data-driven hydrological models, as well as experience in operationalising real-time hydrological systems and programming in, for example, Python. In addition, you are expected
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drought early warning and monitoring system for large-scale river basins. The project will explore both data-driven and model-based approaches for drought predictions, paving the way for a continental high