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combination of classical signal processing methods with state-of-the-art machine learning techniques, and you will thus find yourself in the intersection between emerging research domains and innovations, where
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neuro-adaptability with changes in cortical manifestations during an intervention (e.g., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) will
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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
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(P2X) application. The outcomes will support reliability testing time reduction in design phase and predictive maintenance in the operation phase. The integration of machine learning assisted approach is
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us to explore the relation between the degree and type of processing, and the foods that result from their use. The results will be used for data machine learning, in collaboration with other partners
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on data-intensive systems, spatio-temporal data management, data analytics, and applications of machine learning, with applications in digital energy and intelligent transport. International evaluations
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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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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