Postdoctoral Researcher in Computational Statistics and Population Modeling

The Swedish Museum of Natural History is a government agency with a mandate to promote knowledge, research and interest in our world. It is a prominent research institution and Sweden's largest museum. For more than 200 years, the museum has been collecting specimens and data and conducting research on life on earth. The collections contain more than 11 million plants, animals, fungi, environmental samples, minerals and fossils. All research and knowledge are shared in the exhibitions, Cosmonova and in activities at the museum and digitally.

The Department of Population Analysis and Monitoring conducts research, monitoring, and genetic analyses of marine and terrestrial mammals. The department consists of several working groups focusing on species monitoring, conservation, and genetic identification. Activities include monitoring harbour porpoises, seals, bears, golden eagles, and arctic foxes, as well as carrying out genetic analyses for a vide range of conservation and environmental applications through the Centre for Genentic Identification (CGI). The department currently hosts four posdoctoral researchers and several senior researchers with expertise in ecology, genetics, and environmental monitoring.

1 job(s).

WORK TASKS
Management of large carnivores such as brown bears, wolves, and wolverines requires reliable, transparent, and spatially explicit estimates of population size and dynamics. Scandinavia hosts one of the worlds largest long-term genetic monitoring datasets for large carnivores. However, current statistical methods do not fully exploit the richness of these data.

The BearKin project, funded by the Swedish Environmental Protection Agency, aims to develop scalable and ecologically realistic statistical methods for estimating spatio-temporal abundance of large carnivores, with a primary focus on the Scandinavian brown bear. The project integrates:

• Open-Population Spatial Capture-Recapture (OPSCR)
• Close-Kin Mark-Recapture (CKMR)
• Advanced Bayesian computation and custom Markov-Chain Monte Carlo (MCMC) algorithms
• Spatial models with heterogeneous detection processes
• Web-based tools for transparent dissemination of abundance estimates

All methods will be implemented in R using the NIMBLE framework and integrated into the open-source package nimbleSCR. 

As a postdoc within this project, you will play a central role in the methodological development, including:

• Developing computationally efficient OPSCR models for large-scale monitoring data
• Implementing custom MCMC samplers and reparameterisations in NIMBLE
• Modelling spatially heterogeneous detection processes (e.g. Gaussian random fields)
• Integrating ecological constraints and kinship information into dynamic population models
• Publishing results in leading journals in statistical ecology and computational statistics
• Contributing to open-source software development (nimbleSCR, rovquantR)
• Participating in workshops and stakeholder communication.

You will collaborate closely with Martin Sköld (Swedish Museum of Natural History) and the RovQuant project (Applied Quantitative Ecology Group, Norwegian University of Life Sciences).

QUALIFICATIONS
Requirements: 

• PhD in statistics, computational mathematics or similar or PhD in ecology or similar, with exceptional quantitative and computational skills. It is considered an advantage if the doctoral degree was completed no more than three years prior to the application deadline.
• Documented experience in several of the following areas: Bayesian statistics, hierarchical modelling, spatial statistics, Markov-Chain Monte Carlo methods and  capture-recapture or population modelling.
• Strong programming skills in R
• Experience of preparing peer-reviewed scientific papers
• Proficient in written and spoken English.

Meritorious qualifications:

• Experience with NIMBLE, Stan, or other probabilistic programming frameworks
• Experience with spatial capture-recapture (SCR/OPSCR)
• Experience with genetic or kinship-based population inference
• Experience with development of R packages
• Interest in applied wildlife management and conservation.

We are looking for someone who takes intitiative, is structured, and has strong collaborative skills. You initiate activities and achieve results.You plan, organise, and prioritise your work effectively, and you set and adhere to timelines.  You work well with others and relate to people effectively, listening and communicating in a responsive and professional manner.

Form of employment: Temporary employment.
Duration: 6 months or longer.

Start date as soon as possible. The position is a fixed-term employment for two and a half years. 

We advance our knowledge of the natural world, inspiring to better care of our planet. Our ambition is that the employees of The Swedish Museum of Natural History shall represent the diversity in Sweden and we welcome every applicant.

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