Opportunities

The Virginia Tech Center for Ecosystem Forecasting (www.ecoforecastprojectvt.org) is recruiting a data scientist to build predictive models across multiple ecosystem types. We seek an energetic and enthusiastic team member to join our innovative and dynamic research center to help develop real-time ecological forecasting models, software, and computing infrastructure to inform day-to-day environmental resource management. This position will be a core member of our research center, which includes multiple faculty members, staff, data scientists, post-docs, and students working at the intersection of environmental science and data science. This data scientist position will play a critical role in supporting the Center’s mission to: analyze environmental data; build and share ecological models and software; create and assess a diversity of forecasting methods; translate forecasts for decision support; and engage with forecast users. This position will provide many opportunities for skill development and professional growth within our interdisciplinary collaborative team, which integrates team science, community engagement, and education. 

Responsibilities for this position include: the development and deployment of predictive models; management of cloud-based environmental data and workflows; development of open-source software and open training materials; and supporting the computational needs of the team

The position can start at Virginia Tech’s Research Specialist 1 or 2 level depending on prior experience, with a salary commensurate with experience and expertise. This position is in-person, based on Virginia Tech’s campus in Blacksburg, Virginia, USA.

Candidates should submit a cover letter addressing how they meet the required and preferred qualifications described below that includes a link to their software portfolio (e.g., GitHub profile), a resume/CV detailing relevant work experience, and a list of three references with their contact information. This position is funded for at least two years, contingent upon satisfactory performance reviews. The position will remain open until filled. Application review will begin on 02/15/2024.

Required qualifications:

1. Bachelor’s degree in data science, statistics, computer science, or related field
2. Must have excellent teamwork and collaborative skills to work effectively on data science and computational research
3. Proficiency in R 
4. Experience with command line interface
5. Experience with collaborative coding using Git and GitHub and demonstrated by their software portfolio (e.g., GitHub profile).
6. Experience with data visualization in R or similar language
7. Excellent and demonstrated organizational and efficient time management skills
8. Excellent communication skills, including verbal, written, and graphical communication skills

Preferred qualifications:

1. Master’s degree in data science, environmental science, statistics, computer science, or related field
2. Experience working in a highly interdisciplinary collaborative team
3. Substantial experience with R 
4. Proficiency with Python 
5. Experience analyzing environmental data and/or models
6. Experience with software development (e.g., demonstrated by R package development).
7. Experience analyzing time-series datasets
8. Experience with machine learning models
9. Experience in likelihood or Bayesian statistical analyses

A new position for a postdoctoral research associate is available within the Virginia Tech Center for Ecosystem Forecasting (www.ecoforecastprojectvt.org). The Center is a vibrant, highly collaborative, interdisciplinary research center developing real-time ecological forecasting models, software, and computing infrastructure to inform day-to-day environmental resource management. This position will have the opportunity to lead publications emerging from many ongoing forecasting projects, including water quality forecasting (flare-forecast.org), the National Ecological Observatory Network Ecological Forecasting Challenge (www.neon4cast.org), and forest productivity forecasting. We envision the postdoc will leverage these efforts to creatively advance the field of ecological forecasting in aquatic and/or terrestrial ecosystems. Research activities will include: generating aquatic and/or terrestrial forecasts with National Ecological Observatory Network, Forest Inventory and Analysis, and National Park Service data; developing cyberinfrastructure to support forecasting and automated analyses; leading communicating forecasts and research findings via peer-reviewed publications; and engaging forecast users, including the National Parks Service. The position is in-person, based on-campus at Virginia Tech (Blacksburg, VA, USA). The position start date will be flexible, aiming for Spring 2024. Funding is available for two years, contingent upon successful annual reviews. See https://careers.pageuppeople.com/968/cw/en-us/job/528281/postdoctoral-associate for more information and the application link.

Required Qualifications:

1. A Ph.D. in ecology, environmental science, or a related field
2. Experience in publishing peer-reviewed journal articles
3. Significant quantitative research experience in ecological forecasting, statistics, or data science.
4. Experience working in the R programming language
5. Experience with collaborative coding projects and using versioning control tools (e.g., Git)

Preferred Qualifications:

1. Substantial experience in publishing peer-reviewed journal articles
2. Experience in likelihood or Bayesian statistical analyses
3. Experience with machine learning models
4. Substantial experience in R or equivalent programming languages (e.g., Python)
5. Experience in analyzing long-term and high-frequency sensor environmental data
6. Experience with open-science workflows (e.g., Docker Containers, R package development, Linux command-line environments)
7. Demonstrated ability to work in interdisciplinary, collaborative teams

We are recruiting two graduate students working at the intersection of freshwater ecosystem science and data science to start at the Ph.D. level in 2024

We are always looking for enthusiastic undergraduates to assist with computational projects.