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of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine learning in general. OCBE
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such as mechanistic, chemometric, deep learning, and physics-aware models. Improve robustness and reliability of the developed methods for deploying AI models in real environments utilizing augmentation
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, metabolic measurements, muscle biopsy allocation and analysis, etc.), data analysis, and dissemination of findings. The candidate will work closely with researchers at NIH and external collaborators
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and ML libraries (e.g., NumPy, SciPy, PyTorch/TensorFlow, scikit-learn). Experience with spatial data analysis, interpolation techniques, or geostatistical concepts variography, kriging, conditional