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, signals, measurement data, and biomedical data. Current research topics include: causal analysis in time series complex systems and networks, chaos, and fractal-based methods, EEG analysis, computational
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. Performs advanced signal processing and time–frequency analyses of neural recordings. Develops and implements computational pipelines using MATLAB, Python, or similar platforms. Leads manuscript preparation
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clinical translation in head and neck health. FoDOCS integrates expertise across bioscience, biomedical and optical engineering, clinical practice, and data science, enabling seamless translation from
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Christmas apart. Selection process The evaluation of the CV of the candidates will focus on the adequacy of previous work experience within the scope of the call (point 3). The Selection will be based
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Science, or a related field. Demonstrated experience in image or signal processing, instrumentation data pipelines, or similar. Strong programming skills in Python, R, or MATLAB. Experience with statistical
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implementation of data pipelines. Programming in computational neuroscience: Python, MatLab. Specific Requirements Knowledge: Processing of neural signals. Network science. Statistical learning. Algorithm design
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preclinical imaging - Proven expertise in quantitative imaging analysis and in signal or image processing - Strong analytical, organizational, and communication skills - Ability to work in interaction with
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 15 days ago
‑infrared optoacoustic sensor (MiROS) for non‑invasive in‑blood biomarker detection. Your tasks Develop data analysis pipelines and signal processing methods for mid-infrared optoacoustic sensing Translate
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-nondiscrimination . Minimum Required Qualifications Ph.D. in physiology, biochemistry, biomedical sciences, or a related field. Strong experience in biochemical and molecular biology techniques, tissue processing and
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant