This project works with a novel corpus of text-based school data to develop a multi-dimensional measure of the degree to which American colleges and universities offer a liberal arts education. We seek a data scientist for various tasks on a project that uses analysis of multiple text corpora to better understand the liberal arts. This is an ongoing three-year project with opportunities for future collaborations, academic publications, and developing and improving existing data science and machine learning skills. Tasks likely include: (1) Using Amazon Web Services to create and maintain cloud-based storage (SQL, S3 buckets) of the project’s expanding library of data. (2) Extracting information (named entities, times, places, books, and so on) from millions of plain-text syllabus records. (3) Merging multiple forms of data into a single dataset. (4) Scraping websites for relevant information (e.g., college course offerings, school rankings). Some pages may include dynamically created content that requires the use of a program such as Selenium.

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The human microbiome is associated with different diseases, but the metabolic mechanisms through which it can modulate health are mostly unknown. Understanding these mechanisms is of paramount importance for prevention and treatment. While metagenomics analysis provides associations between microbial presence and specific diseases, metabolomics analysis can highlight metabolic alterations. None of the two, however, can unveil microbiome metabolic mechanisms associated with these detected alterations. In an attempt to fill this knowledge gap, several microbiome metabolic modeling methods were recently developed. An accurate evaluation of the accuracy of such methods in relation to different pathologies and microbiomes was never conducted.

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Single cell sequencing has generated unprecedented insight into the cellular complexity of normal and diseased organ. We are interested in using this technique to understand the mechanisms of eye development, disease and regeneration. We also would like to compare the transcriptomic signatures between mouse models and human tissues. This project involves analysis of large amount of data from single cell sequencing. It requires understanding of statistical analysis and proficient programming skills.

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We are conducting a large-scale study analyzing brain tissues from mice and humans with different APOE genotypes, using both single-nucleus sequencing and spatial transcriptomics to assess RNA expression differences caused by APOE genotype. We are working with an expert bioinformatics core, but would like a data science student to help perform the analyses and act as an in-lab lead for the bioinformatics analysis. Prior experience analyzing RNA-sequencing data is preferred, but not required.

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The proposed project would focus on analyzing quantitative data from a 4 year NIMH-funded study entitled “Integrating evidence-based depression treatment in primary care: Tuberculosis (TB) in Brazil as a model” (PI: Sweetland, K01MH104514). The aim of the study was to assess whether social network analysis could be used to leverage the receptivity and connectivity of TB providers in a Brazilian public health system in a way that could accelerate the adoption (implementation) and diffusion (dissemination) of an evidence-based treatment for depression treatment in a primary care network. Baseline receptivity was operationalized via six brief quantitative scales to measure mental health literacy, work self-efficacy, organizational climate, attitudes towards evidence-based practices, organizational readiness to change and individual innovation thresholds. Connectivity was assessed by asking TB providers with whom they discuss difficult cases, give advice to, or receive advice regarding difficult TB cases. Baseline receptivity and connectivity data was used to identify 3 pilot sites in which to train primary care providers to deliver evidence-based depression treatment for one year.

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We need someone with strong data wrangling capabilities, to be able to determine quick ways to clean and merge data. The format of the data is spatial (GIS) but it could also be manipulated in tabular format. GRID3 is a program within CIESIN which is a research center located at the Lamont-Doherty Campus (with office space on the morningside campus) and is part of Columbia’s Earth Institute. Candidates can learn more about the program at the GRID3 website.

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Our lab is using clinical notes to phenotype COVID patient outcomes. The aim is to better understand the sequela of COVID-19 from clinical notes.

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