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. - Skills and aptitude in survey methods, plot monitoring using sensors, and modeling - Knowledge of temperate or tropical agroforestry systems - Proficiency in R & Python coding and Supervised Machine
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are working as Peer Leaders who foster collaborative and active learning environments for undergraduates in a variety of classes. To model this educational setting, the Pedagogy of Peer-Led Learning course is
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. • Design, implement, and train machine learning models for tasks such as object detection, recognition, and image segmentation. • Develop scalable training pipelines and optimize models for real-time or
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 23 days ago
was installed at IT4Innovations in 2025. For more details, see www.it4i.eu . Activity description: · modelling and optimization of electrical networks using open source tools (preferably Julia or Python
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patch management of computers. Creates scripts to automate services. Creates baseline software sets, adhering to department and District standards, for various computer makes and models; builds, tests
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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
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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. The positions focus on applied machine learning methods for real-world systems. Possible research directions include: Transfer learning and domain adaptation across heterogeneous production environments (e.g
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software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical