We’re Hiring: Quantitative/Computational Research Assistant
Ogbunu Lab, Department of Ecology and Evolutionary Biology, Yale University
August 9, 2021
Position
The Ogbunu Lab at Yale University is recruiting a fully-funded Research Assistant (RA) to work on a set of projects at the intersection of data science, modeling and computation, for projects ranging in scope from epidemiology, to population genetics, field ecology, and computational social science.
The RA would be considered a full-fledged member of our research group, and would participate in original research projects. It is expected that the candidate’s research would contribute to published research.
(see: “Citations of relevant work” below for more information)
Research Keywords
Computational and mathematical modeling, data analysis, data science, data visualization, data management, programming
Terms and specifics
The expectation is for an in-person position, for research to take place on site in New Haven, Connecticut, USA. The initial appointment is for one year, renewable upon review.
The start date is flexible. Salary is negotiable, but will be based on experience. There is no firm deadline: Interviews will continue until the position is filled.
Scientific Description
The ideal candidate would have experience with a range of computational and/or mathematical techniques.
Skills to be emphasized: data analysis, statistics, mathematical modeling, data visualization, data science. The candidate need not be an expert at all of these, but rather, have a strong enough background that they can build on their skills.
Qualifications
Bachelors degree or equivalent in one of a number of fields related to science. Practical experiences are valued. Programming experience is required. Experience with mathematics and computer science coursework is strongly recommended.
While mostly we use python and R in our current projects, I am more interested in identifying a strong candidate with respect to talent, perspective and personality rather than along a specific computational language or tool.
Personal Characteristics
Candidate must be comfortable with multi-tasking, be broadly curious, and interested in exploring important questions across disciplines.
The ideal candidate would be motivated, organized, and precise. While they should feel comfortable with independence, they should also enjoy being a member of a research program where communication and collaboration are prioritized.
While many personality-types are welcome, decency, professionalism, and generosity are absolute requirements. Those driven entirely by competition and self-interest, rather than curiosity, will be a poor fit.
Other aspects
Mentoring and professional development are additional aspects of the traineeship: The PI (Ogbunu) and RA will engage in concrete discussion in these areas. In addition, all members of the Ogbunu Lab are strongly encouraged to participate in outreach, activism, scientific communication, or other activities at the intersection of science and society. Self-care is very high priority.
Diversity and Inclusion
Candidates that offer diversity along many dimensions are encouraged to apply. I especially encourage applications from individuals from “non-traditional” backgrounds — those with interesting or unusual life experiences or career pathways (however defined; candidates are free to mention this in their application materials). The position is open to individuals living anywhere in the galaxy (e.g. every country, anywhere).
Formal Application Link: https://bit.ly/66418BR.1
Send questions or thoughts to: Brandon DOT Ogbunu AT yale DOT edu.
I can also be reached via social media (Twitter & IG): @big_data_kane
Citations of relevant work.
Note: several of these manuscripts have co-authors who were former quantitative RAs in the Ogbunu Lab.
Ogbunugafor CB, Meszaros VA, Miller-Dickson MD, Gomez LM, Murillo AL, and Scarpino SV. Variation in microparasite free-living survival and indirect transmission can modulate the intensity of emerging outbreaks. Scientific Reports 10, 20786 (2020). Available here.
Meszaros VA, Miller-Dickson MD, Ogbunugafor CB. Lexical Landscapes as large in silico data for examining advanced properties of fitness landscapes. 2019. PLoS ONE 14(8): e0220891. Available here
Miller-Dickson Miles D., Meszaros Victor A., Almagro-Moreno Salvador, Ogbunugafor CB. Hepatitis C virus modelled as an indirectly transmitted infection highlights the centrality of injection drug equipment in disease dynamics. 2019. Journal of the Royal Society Interface. Sep 27; 16(158): 20190334. Available here. (Press coverage by Brown University & Science Daily)
