384 machine-learning-"https:" "https:" "https:" "https:" "https:" Fellowship positions
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of extrusion systems, reinforcement strategies, construction detailing, and construction scale experiments. RA3) Machine Learning and Optimisation for Digital Construction: Data-driven and simulation-based
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machine learning, particularly convolutional neural networks (CNN) and siamese networks; English language proficiency. Requirement for granting the fellowship: The applicants may apply without prior
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. Demonstrated experience applying machine learning and AI-based approaches to empirical disease, ecological, or biological datasets, with an emphasis on pattern identification, prediction, or spatial risk mapping
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within a Research Infrastructure? No Offer Description As a University of Applied Learning, the Singapore Institute of Technology (SIT) works closely with industry in its research pursuits. This position
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publications in high-impact venues. Experience with machine learning frameworks (e.g., PyTorch, JAX) and / or computational materials methods is essential. Additionally, the candidate should possess an excellent
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evaluating the efficiency and accuracy of both physics-based models and machine learning techniques, leveraging high-performance computing resources. The role will involve collaboration with leading
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degradation that could occur to C-130 crew members from extended exposure to environmental insults. This research will also inform the development of effective human-machine systems and healthy Airmen protocol
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(e.g., Docker, Kubernetes, cloud/edge environments). Demonstrated expertise in AI, distributed computing, machine learning, or systems software design. Strong background in software engineering
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, and formulation of clinical study design, image processing, machine learning, and statistical analyses to illuminate specific research questions. Among the machine learning techniques, deep learning
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, and the military. Both quantitative and qualitative approaches would be relevant, and comparative approaches (cross-sector, cross-institutional, cross-national, or other) are welcome, but not required