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Field
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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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College of Agriculture and Life Sciences (CALS), where full-time faculty and other renowned professionals direct world-class, mission-driven programs. Staff at the Lab teach undergraduate courses, advise
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postdoctoral appointment in Remote Sensing of the land surface, with a strong interest in the integration of geospatial Artificial Intelligence (AI) and machine learning. Are you enthusiastic about the chance to
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, museum professionals, and other partners in the Hybrid Intelligence network. Where to apply Website https://www.academictransfer.com/en/jobs/362707/postdoc-position-on-multimodal-… Requirements Specific
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at Carnegie Mellon University invites applications for a postdoctoral researcher to lead projects at the interface of computational chemistry, machine learning, reaction mechanism elucidation, and automated
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network inference and modeling, machine learning and deep learning. Experience in working with Arabidopsis and plant genome data is a strong plus. The position is expected to continue for multiple years
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Hey machine learning enthusiast with a love for physics and
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theoretical challenges motivated by astrodynamics, optimisation, control, scientific machine learning, mission design and autonomous systems, translating these into new research directions in deep learning
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applications. Research activities may involve geophysical forward modeling, AI-driven geophysical inversion, seismic monitoring and imaging, scientific machine learning, depending on the specific research focus
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://postdoc.wustl.edu/prospective-postdocs-2/ . Lab website: https://cruchagalab.wustl.edu/ . Research Projects: Plasma, CSF and Brain Proteomic analysis. Biomarker identification through the use of machine learning