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Stillingstittel:
PhD Research Fellowship in Vegetation modelling using Machine Learning

Stillingsbeskrivelse:
Jobbnorge ID: 166313

Job abstractJV_description

Position as PhD Research Fellowship in modelling vegetation-atmosphere interactions in northern environments using Machine Learning available at Department of Geosciences, University of Oslo (http://www.geo.uio.no).

No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo. Starting date no later than 01.10.2019.

The fellowship period is up to 4 years, with 3 years devoted to research education. The position entails a compulsory work load of 25% that consists of teaching and supervision duties, outreach and research assistance.

More about the position

The successful candidate will work in the LATICE ("Land-ATmosphere Interactions in Cold Environments") research group with scientists with expertise within various disciplines related to observing, analyzing and parameterization of the land surface. LATICE is recognized as a strategic research area by the Faculty of Mathematics and Natural Sciences, dedicated to improving our knowledge on cold environment processes and their representation in Earth System Models (ESMs). LATICE plays a key role in integrating the research and field activities of the various disciplines, including the atmosphere, the hydrosphere, the biosphere and the cryosphere. The PhD candidate will be part of a motivated research team with a high number of early career scientists working together in a highly interdisciplinary environment. For more information, see http://mn.uio.no/latice/.

LATICE seeks to improve the process understanding and model representation of key features of the northern environments, such as snow, permafrost, seasonal frost and high latitude vegetation. Vegetation interacts with climate through albedo, roughness, evapotranspiration, CO2 sequestration and by influencing snow accumulation and ablation. A key challenge is the parameterisation of high latitude vegetation, including the lack of representative Plant Functional Types (PFTs) for e.g., shrubs, tundra grasses, as well as PFTs dominated by mosses or lichens. The role of snow-vegetation-soil interactions and feedbacks are also central, influencing both the energy and the water balance. Field campaigns combined with other observational datasets, such as remote sensing products, are vital to provide justifiable and representative vegetation parameters for large areas and different applications.

Dynamic vegetation models are particularly parameter-heavy, and most parameters have large uncertainties and observations needed for validating model results (e.g., site observation of plant composition) exist, but not necessarily continuous in space and time. In addition, observations are not always accurate or comparable with the scale of model output. This makes it particularly challenging to constrain vegetation models with observations in a meaningful and efficient way. The PhD project will use Machine Learning (ML) and advanced statistical tools to address these challenges. More specifically, it will explore key governing environmental factors (temperature, precipitation, snow, climate extremes) controlling vegetation properties (based on observations) and compare the relationships with modelled results. It further aims to build a data assimilation framework with ML approaches to more efficiently constrain model parameters with various available observations.

Model platforms used within the group include, but are not limited to, the land surface model CLM, the hydrological model Shyft, as well as statistical based models such as ecological- and distribution models in R (including machine learning methods), backed by spatial data on GIS format; models that are used to develop and test parameterizations for a variety of processes. The candidate will interact with model developers within LATICE and contribute to model testing and improvements with a special focus on the parametrization of vegetation. Details of the research plan will be developed based on the successful candidate's own expertise and research ideas in close collaboration with key LATICE researchers. A research visit of 1-3 months to one of our international collaboration partners is encouraged. The PhD will be supervised by an interdisciplinary team of experts and supported by ML activities at the department/MN faculty.

Qualification requirements

The Faculty of Mathematics and Natural Sciences is a leading research faculty. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials.

• Master's degree or equivalent in earth system science, biogeography, ecology or a related discipline (e.g., in physics, mathematics, statistics or informatics with experience in the geo- and/or biosciences).
• Strong background in mathematics/statistics
• Strong background in scripting (e.g. Python, R) or programming (e.g. C++, FORTRAN)
• Foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system
• Candidates without a Master's degree have until 30 June, 2019 to complete the final exam

Grade requirements:

The norm is as follows:

• the average grade point for courses included in the Bachelor's degree must be C or better in the Norwegian educational system
• the average grade point for courses included in the Master's degree must be B or better in the Norwegian educational system
• the Master's thesis must have the grade B or better in the Norwegian educational system
• fluent oral and written communication skills in English evt. and a Scandinavian language

http://www.mn.uio.no/english/research/phd/application/application.html

Desired qualifications:

• Relevant (topic of the announcement) hydrologic or land-surface modeling experience preferable from high latitude environments
• Field observations, instrumentation, or campaign experience
• Knowledge of large data handling and analysis
• Knowledge of a Scandinavian language

We offer

• Salary NOK 449 400 - 505 800 per annum depending on qualifications and seniority as PhD Research Fellow (position code 1017).
• Attractive welfare benefits and a generous pension agreement
• Vibrant international academic environment
• Oslo's family-friendly surroundings with their rich opportunities for culture and outdoor activities

How to apply

The application must include

• Cover letter - statement of motivation and research interests
• CV (summarizing education, positions and academic work - scientific publications)
• Copies of the original Master's degree diploma, transcripts of records and letters of recommendation
• Documentation of English proficiency
• List of publications and academic work that the applicant wishes to be considered by the evaluation committee
• Names and contact details of 2-3 references (name, relation to candidate, e-mail and telephone number)

The application with attachments must be delivered in our electronic recruiting system, please follow the link "apply for this job". Foreign applicants are advised to attach an explanation of their University's grading system. Please note that all documents should be in English (or a Scandinavian language).

Applicants may be called in for an interview.

Formal regulations

Please see the guidelines and regulations for appointments to Research Fellowships at the University of Oslo.

The purpose of the fellowship is research training leading to the successful completion of a PhD degree.

The fellowship requires admission to the PhD programme at the Faculty of Mathematics and Natural Sciences. The application to the PhD programme must be submitted to the department no later than two months after taking up the position. For more information see: https://www.mn.uio.no/english/research/phd/

According to the Norwegian Freedom and Information Act (Offentleglova) information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure.

The University of Oslo has an agreement for all employees, aiming to secure rights to research results etc.

The University of Oslo aims to achieve a balanced gender composition in the workforce and to recruit people with ethnic minority backgrounds.

Contact information

For further information please contact: Professor Olav Skarpaas, phone: +47 22851628, e-mail: olav.skarpaas (at) nhm.uio.no or Professor Lena M. Tallaksen, phone: +47 228 57214, lena.tallaksen (at) geo.uio.no

For technical question regarding the recruitment system, please contact HR Officer; Ørjan Pretorius, orjan.pretorius (at) mn.uio.no

Se annonsen og søk på stillingen www.jobbnorge.no

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jobCategoriesCodes_0: http://data.europa.eu/esco/isco/C2310

abstractJV_contact_0: See jobabstractJV_description

Arbeidsgiver Navn: Universitetet i Oslo

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