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Research Center for Molecular Medicine (CeMM), ÖAW | Graz 12 Bez Andritz, Steiermark | Austria | 2 months ago
; and how these mechanisms can be understood, modelled and ultimately perturbed for biomedical discovery. Two scientific tracks Track 1: Computational Biology / Machine Learning for membrane protein
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community partners to ensure outputs reflect local priorities and inform adaptation planning. Duties may include: Develop spatially explicit computational models using machine learning, hydrologic, and energy
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Science at Johns Hopkins University seeks an outstanding postdoctoral fellow to lead scientific efforts on machine learning applications to signed languages to begin by Fall 2026. The fellow will work
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, protein structure modelling, AlphaFold/multimer-based analyses, statistics, data visualization, and interdisciplinary work at the interface of proteomics, structures and machine learning. Track 2
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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs
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extensive knowledge of robotic navigation, machine learning or other relevant fields experience working with marine robotic systems or the data they collect, including inertial, acoustic, visual and
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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, mathematical modeling, optimization, and scientific computing. Experience in computational imaging, inverse problem solving, machine learning, or artificial intelligence is highly desirable. Excellent
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for tumor behavior and clinical outcomes Development and implementation of artificial intelligence and machine learning algorithms for biologically and clinically motivated questions in pediatric oncology
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and analytics protocols for clinical trial datasets Apply deep learning and AI to time series neuromonitoring data Build and test predictive models using machine learning techniques Drive methodological