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algorithms: exploring and designing training strategies (e.g., supervised finetuning, reinforcement learning, or new alignment protocols) or inference-time scaling methods suitable for low-resource settings
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levels. In this project, the PhD student will learn to understand and apply modern causal inference techniques such as target trial emulation, marginal structural models and G-computation to observational
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inferences are also made from mechanistic insights from animal studies to human medicine. Moreover, the project reflects on the implications of different experimental designs for knowledge generation
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methodologies in machine learning and causal inference applied to human health. Read more about NCRR here . Your job responsibility With a motivated, interdisciplinary team of approximately 70 researchers and
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methodologies in machine learning and causal inference applied to human health. Read more about NCRR here . Your job responsibility With a motivated, interdisciplinary team of approximately 70 researchers and