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machine learning for audio and acoustics - Research in experimental techniques in acoustic -- - Investigación en procesamiento de señales y aprendizaje automático aplicados al audio y la acústica
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Department. How to Apply: Interested candidates may apply online at https://jobs.coastal.edu/postings/122640 Applicants must submit a cover letter, resume and list of three (3) professional references. Review
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to apply Website https://www.jobbnorge.no/en/available-jobs/job/308098/phd-research-fellow-withi… Requirements Research FieldEngineeringEducation LevelMaster Degree or equivalent Skills/Qualifications
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inclusive learning environment that inspires students from diverse backgrounds. You will supervise and mentor BSc, MSc, and PhD students, supporting their development into independent researchers and highly
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environments that aims to address how distributed sensing, fibre-optic monitoring, environmental observations, drone- and satellite-based data, operational infrastructure datasets, and/or machine learning can be
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applies molecular and genomic tools in combination with behavioral manipulations to uncover biological processes that promote and limit the ability to learn across development. The lab uses the zebra finch
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, microplastics; by means of numerical and experimental approaches including high-performance computing, data science methods via machine learning/AI and digital twins, enhancement, development and of application
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related datasets, ensuring data quality and maintaining clear, reproducible analytical workflows. Carry out statistical and machine learning analyses to identify factors, patterns and trends associated with
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. Advanced machine learning, reinforcement learning, and agent-based optimization techniques will be developed to reduce voltage deviations, cut active power curtailment, and improve system adaptability under
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sensing, advanced signal processing, machine learning, and longitudinal behavioral analysis to establish clinically meaningful digital biomarkers of eating behavior. These biomarkers will quantify fine