44 condition-monitoring-machine-learning Postdoctoral positions at Technical University of Denmark in Denmark
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potential for exploiting temperature gradients for producing electricity and predict their long-term performance under real operating conditions. The project also includes modeling of heat transfer and
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the disparities. While foundation models offer great promise for creating more robust machine learning models for a wide array of tasks, it remains an open problem how to foresee their biases across that wide array
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hardware and fluidics. You are expected to: Design and build an automated fluidics system for deposition of chemical reagents with in-situ monitoring of film deposition using quartz crystal microbalance
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, TESPy, or similar libraries. Strong programming skills in Python or MATLAB, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit
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digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design
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surveillance. Strengthening monitoring and tracking systems is essential for identifying sources and transmission routes, thereby guiding evidence-based interventions that improve food safety and protect public
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DTU Energy, we are working to enhance the mechanical robustness of cell and stack components and gain deeper insights into their behaviour under technologically relevant conditions. This includes
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Job Description The Section of Bioinformatics, DTU Health Tech is world leading within Immunoinformatics and Machine-Learning. Currently, we, together with Lonza Cambridge, UK, are seeking a highly
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simulations using, e.g., COMSOL, Lumerical, or other Maxwell solvers. Experience with machine learning algorithms is an advantage but not required. General qualifications Scientific production and research
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interest and documented skills and experience in using computer-based tools to analyse, simulate and predict capture performance of active and passive fishing gears. A track record of publishing in peer