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or PhD in public health, epidemiology, statistics, biostatistics, math, economics, or quantitative social sciences plus two years’ experience preferred. Experience with machine learning, data mining, and
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Computer Engineering. Expertise in computer vision algorithms and image processing techniques (such as object detection, segmentation, and feature extraction). Proficiency in deep learning frameworks such as
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models and theories and inclusive educational design; demonstrated presentation skills; working knowledge of common computer applications (e.g. word processing, PowerPoint, databases) and assistive
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/interventions, and clinical diagnoses. The post would be suitable for applicants with general interests in AI, machine learning, large language models, foundation models, signal processing, computational
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data-driven program evaluation practices. Knowledge of digital learning platforms and emerging learning technologies. Equipment Utilized Personal computer, laptop, and related peripherals Standard office
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that are commonly used today. Using the improved noise models, machine learning methods will be used to enhance the segmentation of EEG data into auditory signal and background activity allowing for refined control
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Transformers). Analysis of existing datasets. Evaluation of the trained models on suitable datasets. What you contribute Good knowledge in the field of machine learning and training neural networks. Good Python
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, designing, implementing, and evaluating ML models that address practical challenges across domains. The researcher will contribute to the development of a full machine learning pipeline, including data
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The VCC center at KAUST is looking for research scientists in Prof. Wonka's research group. The topics of research are computer vision, computer graphics, and deep learning. A suitable candidate
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | 2 months ago
-scale (~10’s of km2) permafrost thermo-hydrological hybrid twin, to be coupled with state-of-the-art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani