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Job Description Are you passionate about leveraging IoT, machine learning, and optimization to make energy districts and communities more sustainable? We are looking for a highly motivated and
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, bioactive compounds, and other key nutrients. Develop and apply machine learning and modeling techniques to analyse, predict, and optimize the effects of processing on food composition, food Ingredient
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and may commence preferably in the fall of 2025. Your tasks As a PhD student at DTU, your work will include: Learn to perform rigorous and relevant research Collaborate with key academic and/or
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focused on applying quantitative tools (e.g., statistics, machine learning, optimization, simulation) to healthcare delivery, healthcare operations and healthcare management as well as medical decision
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qualification, you must hold a PhD degree (or equivalent) in computer science, computer engineering, or electrical engineering. Hardware design in a hardware description language such as Chisel, VDHL, or Verilog
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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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qualification, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning
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candidates with expertise in one or more of the following specialized areas: Machine Learning / Deep Learning Uncertainty Quantification Wind Farm Flow Modelling Wind Farm Control Wind Farm Design Wind Farm
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Communication, Singal Processing, Low Power Electronics, Wireless Sensing, Low-Power System Design, Machine Learning & Edge Inference, Underwater acoustic communication. Furthermore, you have a proven record of
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approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modeling, aimed at uncover the key traits that define successful microbial biofertilizers