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clinical approaches, including: Histopathology and digital pathology (whole-slide imaging, WSI) Quantitative analysis of the tumour immune microenvironment AI-based image analysis, machine learning and deep
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. Research will focus on neural data integration, neural circuit modeling, biologically grounded representation learning, and foundation models for neurobiology. The Postdoctoral Fellow will work closely with
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Experienced in A.I., Deep Learning, and Simulations. Physical Requirements & Working Conditions Degree Requirement Listed degree qualification is required at time of application Posting Information FLSA Status
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CORE A*/A conference paper. We invite applications for a postdoctoral position focused on the development of predictive models for clinical outcomes following Deep Brain Stimulation (DBS) in Parkinson’s
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Knowledge of MLOps concepts, including dataset ops, training pipelines, evaluation frameworks, deployment and monitoring. Understanding of deep learning fundamentals and hands-on experience with model
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or deadline for this position has passed and new applications are no longer accepted. *** Position Description Applications are invited for a postdoctoral fellowship at ENS Lyon in the field of machine learning
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multi-omics data and the use of machine learning and data science techniques. Strong publications record according to his/her career stage. Skills: Excellent programming and scripting skills, with deep
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associated with geography, soil sciences, hydrology, civil engineering, or related discipline, with research expertise in geospatial AI, deep learning foundation models, hydrology, river science. Candidates
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, labs, and clinical events Apply deep learning and transformer-based approaches to longitudinal EHR data Integrate multi-modal data (EHR, labs, vitals, imaging, etc.) • Position 3: Postdoctoral Researcher
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Details Title Postdoctoral Fellow in Neurobiology (Ponce Lab) School Harvard Medical School Department/Area Neurobiology Position Description Postdoctoral fellow in visual neurophysiology and deep