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Field
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computer vision tools (e.g., MediaPipe, OpenPose, homography estimation, optical flow). Experience with eye-tracking data collection or analysis. Familiarity with deep learning frameworks (PyTorch
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/C++, FORTRAN and/or Python. Experience working with geo-spatial information, remote sensing data, and GIS software. Experience in deep learning and computer vision. Experience in developing software
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. Target start date is January 2026. Appointment Start Date: January 2026 Group or Departmental Website: https://woods.stanford.edu/ (link is external) https://sustainability-accelerator.stanford.edu/ (link
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intelligence (including machine learning). Desired Qualifications Publications in health sciences and biomedical journals. Demonstrable interdisciplinary research experience. Deep understanding of machine
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perception systems, using deep learning and simulation-to-real domain adaptation techniques. You will work with a multidisciplinary team, contributing to fundamental and applied research. Your role will
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with deep technical expertise and transferable skills to tackle tomorrow’s challenges. SIT collaborates with industry in our education, while benefitting them with our talent supply and collaborative
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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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, interdisciplinary project. At the end of the project, you will have: a deep understanding of the hydrodynamic processes that control the dispersion of buoyant macroplastic items in the coastal zone; expertise in
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/deep learning with GWAS/sequencing data and other types of omic data, as in Mendelian randomization and TWAS. In addition to new methods development and evaluations, the job responsibilities include
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l'institut du thorax, INSERM, CNRS, Nantes Université | Nantes, Pays de la Loire | France | 2 months ago
using deep learning approaches. Effectively, in order to prevent cerebrovascular accidents, it is important to study the blood flow at the major bifurcations along the cerebral vascular tree (we mostly