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reusable bioinformatics analysis pipelines for processing RNA -seq, single-cell RNA -seq, genomics and proteomics data. Develop novel algorithms and integrated data visualization applications when existing
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reusable bioinformatics analysis pipelines for processing RNA -seq, single-cell RNA -seq, genomics and proteomics data. Develop novel algorithms and integrated data visualization applications when
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Genomics at Harvard Medical School Several positions are available in the Park Lab ( https://compbio.hms.harvard.edu/ ). The aim of the laboratory is to develop and apply innovative computational methods
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and Machine Learning , with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable
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experiments with people and AI systems. Some students may develop new theory or algorithms; others may use existing methods to explain behaviour in a particular setting. In either case, the aim is to
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experiments with people and AI systems. Some students may develop new theory or algorithms; others may use existing methods to explain behaviour in a particular setting. In either case, the aim is to
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analysis pipelines for processing RNA -seq, single-cell RNA -seq, genomics, and proteomics data. Develop novel algorithms and integrated data visualization applications when existing software packages are
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learning” • Participation in ongoing research projects as well as the development of new research initiatives in the fields of mathematics of machine learning, optimal transport, classification algorithms
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CAUSALab, please visit: https://causalab.sph.harvard.edu/. This role is focused on pregnancy research: To evaluate the effects of treatments in pregnancy we emulate target trials using observational
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CAUSALab, please visit: https://causalab.sph.harvard.edu/. This role is focused on pregnancy research: To evaluate the effects of treatments in pregnancy we emulate target trials using observational