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Field
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-FATES model using: Snow cover Flux tower data The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently
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understanding of adaptive immune receptor (antibody and T-cell receptor) specificity using high-throughput experimental and computational immunology combined with machine learning. The long-term aim is to
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(PhD entry Level - $108,156 p.a.) Join a collaborative and cutting-edge research environment working with world-class researchers. Apply statistics, bioinformatics, and machine learning methods
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microphysics, severe storms, and mesoscale meteorology; data assimilation, machine learning, and causal discovery; education and human resources; global biogeochemical cycles and ecosystems; and radiation and
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to the work described herein. Qualifications Job Requirements: • PhD in Biostatistics, Bioinformatics, Computational Biology, or other related fields. • Strong foundation in statistical modeling, machine
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Details Title Postdoctoral Fellow in Deep Learning Theory and/or Theoretical Neuroscience School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position
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using high spatial resolution satellite imagery Exceptional written communication abilities Strong programming experience Familiarity with machine learning algorithms (e.g., DCNN, XGBoost, SVM, Neural
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal
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of Engineering and Applied Sciences. The fellow will design and run human experiments, perform data analysis, and create computational models of learning and memory. A PhD is required. An ideal candidate will be
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it comes with a highly competitive compensation package. The preferred area of research specialization is in Gait/Motion analysis and/or motor control or Learning Sciences in PT education (including