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-based transfer learning classification model for two-class motor imagery brain-computer interface. International Journal of Neural Systems (IJNS). https://doi.org/10.1142/S0129065719500254 * Kudithipudi
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and statistical methods, to large-scale fMRI datasets. The researcher will maintain reproducible codebases, manage data processing workflows, and collaborate with team members on methodological
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are looking for candidates who bring: Expertise in EEG acquisition, signal processing, and data analysis Proficiency in programming Experience in child development research, especially related to auditory
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, and eye tracker data. Work Plan: - Multimodal feature extraction from EEG, HRV, gaze dynamics, and pupil size data; - Signal fusion and model training using interpretable machine learning models (e.g
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engineering, computer science, or related fields. At least 2 years of relevant experiences in similar role / technical hands-on experience in specific scope Advanced signal processing and programming skills
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analysis, fNIRS/EEG, wearable sensors, or signal processing. Proficiency in programming languages such as Python, MATLAB, or Java/Kotlin (for Android). Certifications/Licenses Required Knowledge, Skills, and
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(CDEs), specifically within the context of realistic, incompressible CBF processes. The Postdoctoral Researcher will be part of a team of researchers investigating the impact of CBF through
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– optional. Website: https://www.fraunhofer.pt/en/careers/jobs/aicos_2025_28.html 8. Selection Process Admitted candidates will be subject to a curricular evaluation. Based on the results, the top-ranked
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Imaging Centre (CUBRIC ) is one of the best equipped neuroscience institutes in Europe, housing 3 MRI scanners, including a 7T and a Connectom scanner, a MEG, EEG labs, a brain stimulation lab, and of
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signals and monitor using methods such as auditory brainstem response (ABR), EEG, free-run and triggered EMG and direct cortical motor evoked potentials (MEP), and somatosensory evoked potentials (SEP