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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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with neuroimaging and neural signal processing tools, including fMRI, structural MRI, diffusion MRI, EEG, or related modalities. Strong publication record in AI, machine learning, computational
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experience with large-scale human datasets, machine learning, statistical genetics, causal inference, multi-omics, cardiovascular imaging, programming in Python or R, and scientific writing. Prior experience
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) conferred by start date Demonstrated experience with imaging and/or video datasets Training and experience in machine learning, computer vision, and deep learning methods Excellent English language
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Medicine are seeking to appoint a Postdoctoral Research Fellow to join a project developing and validating deep learning computer vision models to classify mosquito breeding habitat on very high-resolution
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primates or humans – Theoretical neuroscience, machine learning, or AI • Proficiency in Python, MATLAB, or equivalent data‑analysis frameworks. • A passion for big‑picture questions, open science, and
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QUALIFICATIONS: PhD in computer science, electrical/biomedical engineering, statistics, applied mathematics, or a related field. Strong track record in machine learning/deep learning with imaging data
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). Experience applying machine learning or AI methods in these fields is an advantage. The ideal candidate should also be comfortable structuring, linking, and analyzing large datasets that include dense