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the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use similar techniques to make a statistical inference of the population of subhaloes by
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conferences. About You You will be educated to doctoral level in Remote Sensing, Geography, Geoinformatics, Computer Science, Artificial Intelligence, Machine Learning or a related discipline. You will have
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scales, from the genome to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning
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, and openly release evaluation code. What is Required: A recent Ph.D. (within the last 1-2 years) in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Statistics, or a related
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Challenge grant. The successful candidate will work closely with the Principal Investigators (PIs) to develop and implement innovative research integrating machine learning, computer vision, and wildlife
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programming languages. Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation. Familiarity with machine-learning or deep-learning methods for medical-image
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streams with perturbation signatures and fit these. For these fits, we will explore the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use
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experiences on computer vision • Strong programming skills in using Deep Learning tools like PyTorch and GPU clusters. • Good written and verbal communications. • Open to Fixed Term Contract
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Evidence of research achievement at the very highest international level, including a strong record of publications in top-tier venues in AI/machine learning and evidence of significant influence
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of environmental factors of 60,000 subjects across multiple time points. Our research laboratory has great computing capacity, including multiple H100 and A100 GPU systems for deep learning, and computing clusters