PhD Candidate in Modelling Marine Larval Dispersal and Spatial Population Genetics and Demography

NTNU - Norwegian University of Science and Technology · TRONDHEIM, NORGE · 2 days ago
junior / graduatead in EnglishScience & Researchvia arbeidsplassen.no
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This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here.     Video: https://www.youtube.com/watch?v=_KHQjc4ndas&t=41s About the position A fully-funded PhD position in modelling marine larval dispersal and spatial population genetics and demography is available at the Department of Biology, Norwegian University of Science & Technology (NTNU) under the supervision of Associate Professor  Scott Burgess . NTNU’s Department of Biology hosts internationally leading research activity in ecology, evolutionary biology, physiology, marine science and technology, and cell & molecular biology.  As a PhD Candidate with us, you will work to achieve your doctorate, and at the same time gain valuable experience that qualifies you for a further career in higher education and research, in and outside academia. About the project The PhD position is aligned with a research program that has the overall aim of providing new understanding of the processes that affect marine larval dispersal and gene flow, how we measure it with genetic data, and how we use that information to predict the genetic and demographic responses of spatially structured marine populations to environmental variation and change. Specifically, the primary focus of the PhD will be to understand how complex coastlines, ocean circulation, and biology (e.g., reproductive systems and phenology, larval behavior and survival) interact to affect the spatial structure of the genetic and demographic properties of marine populations. There is flexibility, and an expectation, for candidates to develop their own line of research within this broad scope. For example, projects could focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal distances from the spatial structure of genetic genealogies across the genome, and the genetic relatedness among individuals; or develop and use biophysical models (hydrodynamics + particle tracking) to understand the demographic and genetic dynamics of marine metapopulations and adaptation to environmental change. The PhD position provides an exciting opportunity to gain broad experience and expertise working at the interface of population genetics and demography, oceanography, and computer science. It will provide broad training in research skills, and will suit a candidate who is curious and is motivated to undertake high-level research to gain skills in computational methods and software tools to understand large, complex biological data. The position will be hosted within a dynamic international research group based at NTNU, with strong links to local and international collaborators. There will be opportunities for conference and workshop participation. The project supervisor will be Associate Professor Scott C. Burgess (Department of Biology, NTNU). Your immediate leader will be the head of department. Duties of the position • Complete the doctoral education until obtaining a doctorate. • Learn and apply advanced statistical and computational methods to analyze genetic data generated from simulations, outputs from biophysical models, or empirical genomic data to answer questions in spatial population genetics and marine metapopulation dynamics. • Contribute to generating and managing reproducible computer code and workflows. • Produce a high-quality PhD thesis, comprising of manuscripts suitable for publication in international peer-reviewed journals. • Participate in international activities such as conferences and/or research stays at foreign educational institutions. • Produce presentations for research dissemination. • Contribute to building a collaborative and interactive local and international research team. • Undertake teaching and other professional activities, as agreed with the Department of Biology. Be prepared for changes to your work duties after employment. Required selection criteria • You must meet the requirements for admission to the faculty’s  Doctoral Programme . In particular, your previous education must be equivalent to a five-year Norwegian degree programme. Your degree must include a major independent project (minimum 30 ECTS) equivalent to a master’s thesis. This corresponds to an undergraduate Bachelor degree and typically a 2-year Master’s degree. • You must have a strong and relevant academic background from your previous studies and have an average grade from your Master's degree study, or equivalent education, which is equal to B or better compared to  NTNU's grading scale . If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you have a weaker academic background, you may be considered if you can document that you are exceptionally suited for a PhD education, for example through relevant work experience and/or peer-reviewed academic works. • Good oral and written communication and presentation skills in English.  • You must have a relevant background in ecology or evolution. • You must have strong quantitative and computational skills. The appointment is to be made in accordance with  NTNUs guidelines for recruitment positions for general criteria for the position. Preferred selection criteria • Experience with computer coding, such as R, Python, or MATLAB, and Linux command-line tools including Bash scripting and running analyses on high-performance computing systems.  • Experience with, or ability to learn, the evolutionary simulation software SLiM ( S election on Li nked M utations), or similar. • Experience applying machine learning techniques, or the ability to learn them.  • Experience using biophysical dispersal models, or the ability to learn to use them. • Experience working with geospatial data and spatial analysis, or the ability to learn. • Experience analysing whole genome libraries, including quality control, sequence alignment, and calculating population genetic summaries. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to:  • You must be able to undertake independent and self-motivated activity. • You must be able to work collegially in teams as part of a collaborative research environment. • Work in a structured way, set goals and make plans to achieve them. • Have good communication skills, and present and discuss your research with other professionals. • Have curiosity and a strong motivation for the research. • Be flexible and open to adjusting the plan for the project as needed. • Key characteristics: reliable, communicative, self-motivated, organized, collaborative. Emphasis will be placed on personal qualities. We offer • An exciting job with an important mission in society • Developing tasks in a strong and international professional environment • Career guidance and follow-up during the PhD period • Open and inclusive working environment with committed colleagues • Working capital that can be used to implement the project • Mentor programme as a new employee at NTNU • Favorable terms as a member of the Norwegian Public Service Pension Fund (SPK) • Free Norwegian language training at a basic level (A2) As a PhD Candidate at NTNU, you will have access to employee benefits . Diversity Diversity is a strength, and at NTNU we aim to be an employer that reflects the diversity in society and that makes use of the potential of the population's collective skills. Our vision is  Knowledge for a better world  and  our values ​​are creative, critical, constructive and respectful . We believe that an organization that is equal, diverse and gender-ba

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