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of industrial processes. In a joint effort of both institutes, the Department AI4Quantum – Machine Learning for Quantum Simulation and Computing and Thermal Energy and Process Engineering are looking for a PhD
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illumination variations, which introduce non-stationary shifts and degrade the performance of conventional models. The project proposes the use of hypernetworks to dynamically adapt the parameters of the gaze
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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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, and in-depth data analysis? We're looking for a fast-learning individual with strong transferable research skills to join our Digital Machining team as a Project Engineer In this role, you'll be
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vision, XR and generative models, specifically for capturing challenging scenarios and training deep learning systems to create better experiences for human users and learners. You will contribute
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Neutral Infrastructure (dfCO2), this role contributes to Program 4: Machine Learning for Carbon Performance (https://dfco2.org.au/program_4 ) that aims to advance the next‑generation AI methods to model
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visualization, multivariate statistics, time series analysis, predictive modeling and machine learning. Considerations: Exceptions to standard rates may apply to courses with unique credit hours, supervision
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services, social services. Experience with electronic health records. Ongoing: Current Basic Life Support Certification. Complete Computer Based Learning (CBLs) annually. Maintain annual competencies as
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of new technologies; a second will focus on policy analysis leveraging machine-learning approaches to investigate the effectiveness of different policies and policy mixes in accelerating technology change
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | about 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