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to undertake fundamental machine learning research for projects aligned to one of more of the following themes: Theme 1: Tackling misinformation, working on attribution and integrity for generative AI. Theme 2
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care models into health policy systems Research Focus Areas The postdoctoral fellow will work with multiple levels of data and analysis, from individual stakeholder experiences to organizational systems
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isolation, microscopy, proteomics, single cell RNA-sequencing, bioinformatics, and/or human stem cells will be viewed as an asset Willing to work with animal models Fluent oral and written communication
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experience in multiple areas as listed above, or at the interdisciplinary interface and / or some expertise in research coordination and running a team would be required Perform the following activities in
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particular interest in those who seek to work at the interfaces between disciplines. Responsibilities Conduct independent and collaborative research aligned with Planet Texas 2050's mission* and engage in
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Together, these research directions seek to reimagine how buildings and cities operate—optimizing energy use, enhancing human well-being, and reducing carbon emissions at scale. We are seeking multiple
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computational efforts across multiple labs at Harvard’s Faculty of Arts and Sciences and Medical School. As part of this effort, the Rubin lab is implementing new methods of studying aging in vitro using brain
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interdisciplinary teams to align AI research with ethical standards and best practices in AI safety. Publish findings in top-tier conferences and journals, contributing to the broader AI and machine learning research
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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which they operate. We have established collaborations across the globe and built a research programme that leverages high throughput, high-resolution sequencing platforms and novel analytical methods