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data linked to multiple large surveys with longitudinal individual-level data, school-level aggregated data, and genetic data. The successful candidate should have prior knowledge of quasi-experimental
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into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word “challenging
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, in vitro models, and large collections of well-annotated clinical specimens. We employ state-of-the-art computational biology/bioinformatics approaches to dissect acute and adaptive responses to RAS
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availability of large-scale genotype data across Europe, along with detailed register and survey data from several countries, to map out how specific genetic factors covary with and interact with our
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 19 days ago
-the-counter and prescription drugs, including biological therapeutics and generic drugs. This effort covers more than just medicines. Research Project: The objective of this research is to apply large language
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quantum computing, aiming to solve small, complex sub-problems using Quantum Process Units (QPUs) while leveraging classical High-Performance Computing (HPC) on data-intensive tasks. With dedicated
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successful candidate will work in the AI in Medicine Lab, applying machine learning to cutting-edge problems in cancer genomics, transcriptomics, spatial transcriptomics, and large-scale bioinformatics
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into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word “challenging
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. · First author of one or more scientific peer-reviewed publications. · Experience working with and jointly analyzing large spatial data sets. · Willingness and demonstrated ability to work collaboratively
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, Artificial Intelligence , Bayesian Statistics , Big Data , Scientific Machine Learning , Social Sciences , Biomedical Informatics , Causal Inference , Computational Social Science , Data Science and