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. The opportunity: We are looking for someone to analyse environmental soundscapes using signal processing or machine learning. Initially, you will work with underwater passive acoustic sound data, and later may
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communication signals on our mental processes. What you will do Build novel deep learning architectures for auditory prediction (speech and/or music) prioritizing explainability and cognitive hierarchies Quantify
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behavior and/or brain physiology. A strong analytic background, particularly in modeling neural systems and analyzing large datasets. Experience with acoustic signal processing, sound recognition, and
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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, probability, and statistical signal processing Familiarity with antenna array signal processing techniques Sound understanding of optimization methods and algorithms Proficiency in Python programming Proven
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Qualification requirements PhD or equivalent academic qualifications with a specialization in computational musicology, computer/data science, informatics, sound and music computing, or other related field
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Profile A PhD in (bio)acoustics, spatial ecology, marine environmental science, or a related subject Experienced in acoustic propagation modelling, signal processing, and data mining A strong background in
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strategies. People may knowingly forgo the optimal solution because selecting it would damage their reputation or signal undesirable traits to others. To formally test this idea, the project develops and
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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and sensitivity to light and sound, contributing substantially to global disability. We are launching a translational project combining generative AI-driven protein design with experimental and clinical