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models of healthy and pathological aging. Skills: The successful candidate must have experience: Conducting, analyzing and publishing work using resting state fMRI/task-based fMRI/diffusion MRI methods
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datasets [e.g. behaviour, simultaneous EEG-fMRI and eye-tracking data]. Main research themes include, but not limited to: reinforcement learning and valuation, risk and uncertainty, confidence and
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), automated testing, and documentation for open-source research software. Preferred Qualifications: ● Experience with neuroimaging data modalities (e.g., EEG, fMRI) and familiarity with how empirical neural
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software. Preferred Qualifications: * Experience with neuroimaging data modalities (e.g., EEG, fMRI) and familiarity with how empirical neural data can inform or validate computational circuit models
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, including high‑resolution structural imaging and task‑based fMRI methods. In addition to contributing to all phases of the research, the Postdoctoral Fellow will take a leadership role in disseminating
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audio, large datasets, and multimodal neural recordings (EEG, fMRI, intracranial) to develop models with a major goal of interpretability so that we may better understand the influence of complex, natural
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), automated testing, and documentation for open-source research software. Preferred Qualifications: ● Experience with neuroimaging data modalities (e.g., EEG, fMRI) and familiarity with how empirical neural
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and adapting research methodologies, with particular interest in applying and validating AI tools for analysing behavioural, fMRI, and EEG data. Experienced in preparing academic publications and
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, fMRI, DTI, MRS, fNIRS, PET), among other resources. Eligibility criteria for postdoctoral research fellowship: Doctoral degree in related field. Excellent verbal and written communication skills. Ability
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analysis (e.g., fMRI, structural MRI, functional connectivity) • Experience with longitudinal or developmental datasets • Experience with digital phenotyping, wearable sensors, or sleep research • Experience