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Field
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at the intersection of marine ecology, ocean technology, machine learning, and high-throughput biological imaging. This position offers a rare opportunity to help pioneer the use of advanced shadowgraph imaging systems
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, imaging and physiology, machine learning, drug discovery. The work for Track1 will initially be connected to CeMM in Vienna and will develop toward the Cori Institute in Graz, where the Superti-Furga
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the research themes of software engineering, engineering computing, sensor networks and measurement technology, grid computing and physics data analysis, machine learning, and interactive and collaborative
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or connectivity analysis; machine-learning or deep-learning methods for geospatial analyses; ecological or remote-sensing fieldwork, particularly in alpine environments; Google Earth Engine, geodatabases or cloud
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Principal Investigator and a cell-culture specialist in a friendly, multidisciplinary group spanning optics, electrophysiology, microfabrication and machine learning, collaborating with partners
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insurance, supported by INESC TEC. 2. OBJECTIVES: • Research novel deep learning models for anatomically structured EGGIM estimation.; • Develop methods for image-level and examination-level reliability
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verbal, written communication skills In-depth knowledge of deep learning, specifically VLM models, computer vision techniques (e.g., open vocabulary object detection, model distillation, VQA, test-time
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and management, machine and deep learning, as well as a solid understanding of wave phenomena and geophysical data analysis and imaging.
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a new computational paradigm that combines the versatility of the digital computer with the efficiency of close-to-physics computing. The group targets the full computational stack, from materials
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outside academia. The tasks include extensive use of optical diagnostics, such as: Near and far field high-speed microscopic imaging Chemiluminescence-based techniques Schlieren imaging and shadowgraphy