Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
hold a PhD in acoustics, mechanics, signal processing, instrumentation, computer science or a closely related discipline. Experience in NDT, SHM, data analysis, applied artificial intelligence or
-
at Earth Species Project: Develop AI pipeline for acoustic data processing 3 months at Kyoto University: Hypothesis testing with Okinawan data 2 months at Think Nature: Use sound/network data as
-
least master's-level training in biomedical engineering, electrical engineering, signal processing or equivalent training, with a strong foundation in applied mathematics and programming. • Hands-on
-
neuroprostheses • To study and define 'signature' electrical signals associated with neurodegenerative diseases, particularly in the context of hallucinations • To identify suitable signal processing
-
and multisensory XR at the intersection of acoustics, signal processing, XR, and human perception. You will develop and evaluate physics-based and data-driven audio rendering methods within the
-
Earth science, signal processing, and computational data analysis; demonstrated written and verbal communication skills; and the ability to work both independently and collaboratively with individuals
-
Delft University of Technology (TU Delft) | Delft, Provincie Zuid-Holland | Netherlands | 30 days ago
Engineering, Physics or Mathematics, an area relevant to the subject, good analytical skills and a strong interest in performing experiments is required. a solid knowledge of signal processing and mathematical
-
processing. The candidate must be able to work independently and have knowledge of MATLAB programming and signal/image processing. Experience or skills in medical applications are appreciated. Please
-
sensors; - Knowledge of scientific programming, preferably Python and/or MATLAB, applied to data processing, signal analysis, visualisation of results or development of experimental routines; - Experience
-
working in this cross-disciplinary domain. Specific research topics to apply to bioacoustics might include: low-footprint machine learning; acoustic signal processing enhanced by ML; human-in-the-loop