We are continuously evaluating potential postdoctoral researchers to join our interdisciplinary data science team, spanning multiple research areas in ecology and evolutionary biology. The postdoctoral researchers will join a collaboration among eight faculty at the University of Wyoming, University of Montana, and University of Nevada-Reno, including Drs. Alex Buerkle, Christopher Weiss-Lehman, Lauren Shoemaker, Sarah Collins, and Daniel Laughlin (UW), Joanna Blaszczak and Matt Forister (UNR), and Bob Hall (UM). The team includes 13+ postdoctoral researchers at a time.
Dramatic increases in the scale and availability of data are profoundly reshaping all domains in the life sciences. Data acquisition and availability from DNA sequencers, environmental sensors, parallel global studies, and imagery are outpacing our capacity for analysis, including the development of models that represent our knowledge of biological processes. Research in our consortium will develop and compete computational, statistical, and machine learning methods for multi-dimensional data to create predictive and explanatory models for the life sciences. The project focuses on three research areas: (1) connecting genome to phenome (particularly in the context of evolutionary biology), (2) mechanistic modeling of species interactions and community diversity, and (3) time series of material and energy flux in aquatic ecosystems.
genome to phenome, and evolutionary genetics
species interactions, population dynamics, and community diversity
The positions are 100% research with flexible start dates; however, preference will be given to candidates who will be able to join the consortium immediately. The positions are for two years, with the possibility for extending the appointment, contingent upon performance.
The postdoctoral researchers will be primarily based in one or a few labs but will benefit from the opportunities to collaborate broadly. The positions allow for multiple professional development opportunities, including training in highly interdisciplinary science, collaborations across institutions, regular meetings with the entire consortium, mentorship toward academic and non-academic career development, and interactions with graduate and undergraduate students.
Successful applicants are not expected to have expertise in all facets of the project, but rather may be experts in a given area of modeling or domain of the life sciences. The postdoctoral researchers will primarily analyze existing and simulated data, and will have additional, complementary opportunities for laboratory or field research. We recognize that the best science can originate from diverse collaborations with people from varied backgrounds, and we especially encourage applicants from underrepresented groups to apply. The positions are supported by a 4-year, $6 million NSF EPSCoR RII Track-2 grant in response to our proposal entitled Creating Explanatory, Process-Based Models to Harness the Data Revolution in the Life Sciences.
All positions share the same qualifications.
completion by position start date of all requirements for a PhD in ecology, evolutionary biology, environmental science, statistics, computer science, mathematics, complex systems science, or a related field.
In the cover letter, applicants should state clearly and illustrate how their experience and interests match the following preferred qualifications.
record of publishing in peer-reviewed literature
excellent verbal and written communication skills
experience in at least one of the following research areas: (a) connecting genome to phenome, or other aspects of evolutionary genetics, (b) mechanistic modeling of species interactions, population dynamics, and community diversity, or (c) examining material and energy flux in aquatic ecosystems
has a keen interest in developing skills in mathematical or statistical modeling to extend strong conceptual thinking and research in life sciences
previous interdisciplinary and collaborative work, in addition to project leadership
interest in working with a diverse team across disciplinary boundaries
Applicants may and are encouraged to apply for multiple positions in the consortium. We encourage candidates to contact potential advisors to discuss interests in advance of applying.
When open positions are posted below, one can apply by submitting a cover letter stating your interest in the position and previous experience as it relates to the position, including each of the preferred qualifications. Also, provide a CV, links to 1–2 recent first-authored publications, and names and contact information for three professional references.
We initiated this consortium to address cross-cutting challenges in the analysis and representation of knowledge in the life sciences. In particular, we will learn about, develop, and share innovative approaches to obtain highly predictive and explanatory models. We will use our broad experience in ecology and evolution, and in modeling, to advance process-based understanding in the life sciences. The consortium will meet regularly through video conferencing across institutions and each year will gather in an inspiring location to share what we have learned and plan future work.
From the proposal
The volume and availability of data have increased enormously over the last decade in the life sciences. This expansion is rapidly changing the standard scale of analyses and is leveling and extending access to a broader population of scientists and the public via open-access data. Tremendous opportunities for discovery are being newly created, but these opportunities bring with them substantial computational challenges. In the life sciences, measuring many covariates for each unit of sampling is now common, providing highly dimensional measurements that could be used as variables in predicting biological patterns and processes.
A principal challenge arising from the data revolution is to maximally and efficiently use information in available data and develop mechanistic explanations of the underlying biological processes (Fig. 1). The ideal is to obtain a best fit of a model to the particular data, without including spurious relationships (i.e. “overfitting”) that would not apply to other data, and to obtain highly interpretable models that are required for formalization and generalization in the advancement of science. Whereas the data revolution has spurred great advances in machine learning and statistical modeling, this ideal remains elusive. We propose to address this persistent gap in data science. We will develop innovative, hybrid analytical methods that will allow the development of explanatory, mechanistic models while incorporating the high dimensionality of contemporary data. We will confront our modeling approaches with existing experimental and observational data from multiple biological domains, learn the support for different generative processes, and advance theory in the life sciences.
Figure 1: a) The scale of life science data has revolutionized our ability to assess the contribution of processes (covariates) to biological patterns. b) More complex models trade-off between model fit to current data (green) and predictive accuracy in new conditions (blue). c) Interpretability decreases with the number of parameters. We will minimize these tradeoffs and maximize interpretability (grey).
These fundamental gaps in analytical capacity are mirrored in scientific training and the workforce. Scientific progress is impeded because knowledge of computational tools is insufficient for broad use due to gaps in training in computational and data science. Moreover, rapid methodological changes in response to the data revolution have stymied communication across disciplines in the life sciences. We will address these needs, for scientists at multiple career stages from undergraduate to faculty, build on the existing activities of the Data Science Center at the University of Wyoming (UW), and extend these to the collaborating institutions and beyond. This will include bolstering the success of applicants to the workforce, through: mentoring trainees for successful job searches and interviews, enhanced knowledge of attractive careers, and effective cross-disciplinary training in data science.
The overall aim of the current proposal is to establish a consortium that will fundamentally advance our capacity to analyze challenging, highly dimensional data in the life sciences. This aim will be achieved through three complementary and integrated specific objectives:
Assess, develop, and disseminate innovative computational methods for predictive and explanatory models of high-dimensional data. Drawing on existing data science methods, we will compete models using real and simulated data to assess their performance, extend and develop new methods using a hybrid modeling approach to increase both the interpretability and predictive accuracy of models, and disseminate our results through workshops, freely available published code, and publications.
Formalize life science knowledge in models for three cross-scale domains. We will apply predictive and explanatory models to build process-based understanding of the mapping between genomes to phenotypes, the ecology of species-rich communities and global-scale species distributions, and the interplay of temporal scales in aquatic ecosystem ecology. Our consortium will share effective strategies and iteratively refine models and computational solutions to efficiently advance knowledge across all three domains.
Train and foster the development of scientists for the workforce, through education and inclusion in data science. We will train 12 postdoctoral researchers in cutting edge data science, combined with domain expertise. Our consortium will foster the development of novel, cross-disciplinary collaborations through annual meetings and regular communication. We will provide regular training and professional development to advance the careers of all participants.
While machine learning and computational methods have dramatically changed our capacity for obtaining well-performing predictive models from high dimensional data, many of these methods lack the diagnostic methods and tools to make them interpretable and allow the extraction of general, scientific knowledge that can be applied across systems. This proposal will fund a consortium of researchers who will work across life science disciplines to find, assess, and further develop computational methods that will yield maximally predictive and interpretable models. The consortium will facilitate the careers of several recently appointed professors and a large cohort of postdoctoral researchers, in addition to enabling broad data science education.
The University of Wyoming has strong research programs in ecology and evolutionary biology across multiple departments, including Botany, Zoology and Physiology, Ecosystem Science and Management, Plant Sciences, and the Program in Ecology. The university is located in Laramie, a community that is nestled between the Laramie and Snowy Mountain ranges, which offer ample opportunity for skiing, climbing, hiking, and mountain biking. Laramie has a relatively low cost of living, is close to field sites across a wide variety of vegetation types from mixed grass prairie to alpine tundra, rivers and lakes, and is within easy driving distance of Colorado’s Front Range corridor (Fort Collins, Boulder, and Denver).
The University of Nevada, Reno has strong research programs in ecology, evolution, and conservation biology as well as hydrology across campus, including the Dept. of Natural Resources and Environmental Science, Biology Dept., Dept. of Math & Statistics, and the Global Water Center. The university is at the intersection of the eastern Sierra Nevada Mountains and the Great Basin with incredible access to field sites as well as outdoor recreational activities including skiing, climbing, hiking, and mountain biking. We are ~45 minutes from Lake Tahoe, three hours from Yosemite National Park, and 3.5 hours from San Francisco. The Reno community is also a diverse artistic and cultural community with much to offer.
Flathead Lake Biological Station (FLBS) is year round field station and part of the University of Montana, Missoula. FLBS is located on Flathead Lake, a spectacular environment one hour south of Glacier National Park and 1.5 hours north of Missoula. The Bio Station has full time faculty and staff with research strengths in limnology, oceanography, and conservation genomics. Nearby sites range from glacial ecosystems to Flathead Lake and include a long term research site on a connected river-floodplain on Middle Fork Flathead River. Outdoor recreation opportunities are extensive and include skiing, cycling, rafting, fishing, hiking, and backpacking in one of the largest wilderness areas in the continental US.
The University of Wyoming, University of Montana, and University of Nevada are Affirmative Action/Equal Opportunity Educator and Employers. We are committed to a multicultural environment and strongly encourage applications from women, minorities, veterans and persons with disabilities. Please see each institution’s individual advertisements for details.