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programming, probability theory, and statistical analysis of large datasets using R or Python. A successful candidate should have a Ph.D. in Operations Research, Electrical Engineering, or Industrial
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variety of simulation and optimization techniques. Key areas of interest may include control theory, robust optimization, or distributed optimization. 2. The second candidate will focus on applied research
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cell fate decisions, particularly during early neural development or during the epithelial-to-mesenchymal transition (EMT) in cancer. Our recent work reveals that coding sequences (CDS) and their cognate
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superb quantitative background, strong coding skills (e.g., Python, R), expertise in infectious disease modeling across multiple pathogens, expertise with large datasets and statistical analysis, and high
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sensor integration. Strong coding and debugging skills. Excellent communication, documentation capabilities and a demonstrated track record of publication. An enthusiasm for developing new measurements
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accomplishments, (b) Your broader research interests, and (c) why you are interested in working with us A sample of data analysis code (published or unpublished) A representative writing sample (published
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with electronic health record (EHR) and/or clinical data. Proficiency in Python, with strong coding and debugging skills. Experience with deep learning frameworks such as PyTorch, JAX, TensorFlow
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, robust, and reproducible data analysis. Conventional statistical approaches will be combined with innovations in interpretable machine learning to address each aim from multiple angles. Analysis code will
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glioma models to investigate novel therapeutic strategies and radiation. The goal is to explore a paradigm shift from the somatic mutation theory of cancer to a wound-healing and metabolic reprogramming
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Qualifications: • Doctoral degree with quantitative training (ideally in econometrics) or relevant research experience. • Strong coding skills in R, Stata, or other statistical software package. • Good