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Predicting chemical hazard on the basis of structure, bioactivity, and properties for data-poor substances
| Project number: | CCTE-08-02-2022-01 |
| Lab/Center/Office: | CENTER FOR COMPUTATIONAL TOX & EXPOSURE |
| Division: | BIOMOLECULAR & COMPUTATIONAL TOXICOLOGY DIVISION |
| Branch: | COMPUTATIONAL TOXICOLOGY & BIOINFORMATICS BRANCH |
| Brief description of research project: | For data-poor environmentally relevant substances often little is known about putative hazard. To address these data gaps, the Center for Computational Toxicology and Exposure within the Office of Research and Development has been working to develop computational methods for inferring human health hazard on the basis of chemical similarity via techniques including read-across and (quantitative) structure activity relationships. In this research project, the research fellow will engage in analysis of in vitro bioactivity data, in vivo study data, and/or chemical structural features and properties to further in silico work to predict human health hazard relevant information, such as the dose at which no human health effects would be expected, specific types of hazard such as developmental and reproductive toxicity, and/or molecular events that may be perturbed. Existing predictive tools and structure alerts for hazard may be examined and refined as part of this work. This is a highly collaborative fellowship that will engage with multiple investigators spanning chemistry, toxicology, and informatics and using multiple types of data to develop analyses and tools that will be informative for assessment of new chemicals. |
| Geographical location of position: | Research Triangle Park, NC |
| High priority research areas: | Tiered testing strategies; Building confidence in new approach methods (NAMs); Data availability and accessibility; Decision support and translation |
| Scientific project area: | Bioactivity Data |
| Educational requirements: | PhD in biological sciences, chemistry, or related disciplines appropriate to the position |
| Specialized training and/or experience preferred: | Ideal candidate has a strong background in analyzing large data sets of biological, toxicological, or chemical information using scientific programming languages, such as R, Python, or Java; Any experience with in vitro or in vivo studies or cheminformatics is helpful but not required. |
| Projected duration of appointment: | 3 years |
| Paid relocation to EPA work location: | Yes |
| Application Period Open Date: | Aug 02, 2022 |
| Application Period Close Date: | Sep 16, 2022 |
| Scientific contact/Principal Investigator(s)*: | Katie Paul Friedman, paul-friedman.katie@epa.gov, |
*This person/persons may be contacted for additional scientific information about this project. This person is not authorized to accept applications, make job offers, set salaries, establish start dates or discuss benefits. See general announcement for details on how to apply.