11 machine-learning-and-image-processing Postdoctoral research jobs at Nature Careers
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have experience in computational neuroscience and data mining using machine learning methods. The successful candidate will lead an independent research project dedicated to identifying abnormal neuronal
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microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring. The project
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techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get unprecedented medical
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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of machine learning and health sciences, with unique access to experimental and clinical data. Embedded in Munich’s thriving AI landscape, fellows benefit from world-class facilities, interdisciplinary
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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
Qualifications and Skills Knowledge of microbial metabolic processes and methods to study them (e.g., tracer-based incubations) and general marine/benthic biogeochemistry Interest and intention to acquire and
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The Section of Bioinformatics, DTU Health Tech is world leading within Immunoinformatics and Machine-Learning. Currently, we, together with Lonza Cambridge, UK, are seeking a highly talented and
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, a large initiative funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site
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Lab ). Integrative analysis of single-cell multi-omics and spatial imaging data in cancer, immunology, organoids, etc., in the context of the Human Cell Atlas (single-cell analytics) and/or the ELLIS