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Fund project TARGETWISE. The candidate will be responsible for conducting machine learning omics data analysis within the computational team. The details on responsibilities, obligations and rights
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and summarize results, publish in scientific journals, and contribute to the supervision of students. Your profile PhD degree in bioinformatics, machine learning, computer science, biochemistry
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governments and institutions to achieve outcomes traditional approaches cannot. Job Summary The Bike Shop is hiring a Machine Learning Researcher. The Bike Shop is a CS and Economics research lab focused
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neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
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responsible for the design and testing of original machine-learning based methods for fetal heart biomarker discovery from the CAIFE image and video dataset. The full-time post is funded by InnoHK and is fixed
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closely related field Required Qualifications: - Experience in spectral CT and uantitative imaging including radiomics, machine learning, deep learning or artificial intelligence as applied to radiology
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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Max Planck Institute for Solar System Research, Göttingen | Gottingen, Niedersachsen | Germany | 24 days ago
qualification PhD in fluid dynamics, physics, applied mathematics, computer science, or a related field. Experience with machine learning, numerical methods for PDEs, TensorFlow or PyTorch, and Python. Experience
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road safety analytics framework. The role involves integrating multi-source transport datasets, developing advanced analytical and machine learning models for risk identification, and supporting