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per year for 3.5 years. Lead Supervisor’s full name & email address Dr Massimiliano Fasi: m.fasi@leeds.ac.uk Project summary The growing importance of artificial intelligence is fostering a paradigm
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such as landslide movement style, runout, and how landslide hazards evolve over time. This Ph.D. project will leverage the analysis of new time-series data from cloud-based satellite image archives
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We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only
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. Proficiency in geospatial, 3D, and image-processing software for data collection and analysis. Experience working with GIS, QField, total stations, geophysical equipment, and drones. Experience in stratigraphic
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electronics, including programmable logic devices, especially as applied to low-noise and high-speed sampled systems. Assets 3. Knowledge of the theory of operation of imaging detectors may be considered
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 8 days ago
of critical thinking Experience with photonics, fluorescence, imaging or biochemical methods is helpful Our offer Highly motivated team Flexible working hours World-class institute with outstanding
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procedure Icon Applicants Applicants Icon Foundation Foundation Icon Notification Notification Nomination Examinationapprox. 1-2 months Review Processapprox. 3-4 months Conferral Nomination documents
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histopathology, advanced imaging, gene expression profiling, behavioral analysis, and the application of this protocol to test model- and emerging contaminants suspected to be neurotoxic via endocrine mediated
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, thermal, electromagnetic or kinetic), are critical for the sustainable operation of wireless IoT devices and remote sensors. The world can reduce reliance on batteries and fossil-fuel-derived power if more
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical