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strong background in geomodelling, geophysical and geotechnical investigation, geomechanical engineering, and machine learning. You will be expected to work effectively on a geophysical/geomechanical
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to support the development of AI-driven and data-driven approaches for the discovery and design of functional materials. The role will involve the development and application of machine learning
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Theory (DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area
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collaborative research projects in radiology that include MRI, CT, ultrasound, or molecular imaging. Performs quantitative and qualitative analysis of imaging datasets using advanced computational
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collaborative and international environment fosters innovation, excellence, and impactful research. The Cancer Neuroimmonology, led by Prof. Karen Dixon, is looking for a fully funded postdoctoral position
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manuscripts and scientific presentations and contribute to collaborative publications, grant development and dissemination of findings. · Work closely with investigators and trainees across neuroscience
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innovation at the intersection of Artificial Intelligence (AI) and natural resource/environment management. This position aims to foster collaborative research, including proposal development, across our
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. Using a small number of representative test cases, we will trial Machine Learning-based predictive methods to estimate material condition and indicative reuse potential. The work will clarify data
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. The position contributes to “Brain-in-Space: A Machine Learning Framework for Self-Organizing Low-Earth Orbit Satellite Networks”, funded by the Global Science and Technology Diplomacy Fund. You will
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communication skills. Interest in collaborative biomedical research and multidisciplinary teamwork. Preferred Experience with prediction modeling, statistical learning, penalized regression, or related