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Field
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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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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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, 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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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
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desirable. Experience with AI, machine learning, optimization, spatial analysis, scenario modeling, or decision-support methods is strongly preferred, especially when applied in transparent, interpretable
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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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. - Criterion 2: Knowledge in the scientific areas of the project: Academic or applied knowledge in Software Engineering, Intelligent Systems/Machine Learning, and Interactive Technologies. - Criterion 3
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projects include detecting API misuse, analyzing program performance, measuring energy consumption, detecting security vulnerabilities, supporting library migrations, and leveraging large language models
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implement data management 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