Job Listings

Research Assistant Professor of Bioinformatics

Company:
Loyola University Chicago
Job Location:
Category:
Medical Research
Type:
Full-Time
We are seeking a highly motivated data scientist to join our group, which has been supported by federal funds and institutional grants. The applicant will be responsible for performing hands-on exploratory and regulatory-related biomarkers analysis, large-scale proteomics, metabolomics, and lipidomics analyses, with a primary goal of developing prediction models and identifying molecular endotypes for pulmonary diseases. These respiratory diseases include but are not limit to acute respiratory distress syndrome (ARDS), sarcoidosis, sepsis, and trauma. Most specifically, the applicant will participate in the design of data analysis, implement cutting-edge bioinformatics and biostatistics methods to integrate clinical and trans-omics data. Because this is a full-time faculty position at the research assistant professor position, the applicant is expected to be productive in publications and fundings. This position is for those who plan to apply for their own grants and projects independently. Qualified applicants should have a terminal degree in Biomedical Informatics, Computer Science, Biostatistics, or an analogous field with profound knowledge of data science methodologies, especially in Bioinformatics. This encompasses but is not restricted to, machine learning, deep learning, and network science applied to clinical and omics. The ideal candidate should demonstrate potential for developing a strong independent research program, experience or potential to apply for funding, and proven ability or potential to establish strong collaborations with collaborators and team members. S/he should have demonstrated interest or previous experience in the identification of biomarkers, phenotypes, endotypes, or molecular signatures, have demonstrated capability to forge robust collaborations with basic and clinical researchers, have strong organizational skills and ability to prioritize work, excellent communication skills, and scientific publications, have experience with R, Python, and others for clustering and machine learning algorithms.
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