Lunds universitet, institutionen för naturgeografi och ekosystemvetenskap

Lund University was founded in 1666 and is repeatedly ranked among the world’s top universities. The University has around 47 000 students and more than 8 800 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.

Lund University welcomes applicants with diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset.

Subject description
Dissolved Organic Carbon (DOC) is an important water quality parameter that affects the fate and dynamics of other pollutants in the aquatic environment. The DOC concentrations in many surface waters across the northern hemisphere, particularly in Sweden, have increased during last decades. Increasing DOC concentrations have important consequences for the ecology and biogeochemistry of freshwaters. Therefore, there is an urgent need for improving our understanding of spatial-temporal variabilities (dynamics) of DOC concentrations in surface waters and the processes that drive DOC dynamics; such understanding is essential for improving our ability to predict impacts of future climate and environmental change (e.g. land use change, forest and agricultural management practices) on DOC in order to support decision-making for sustainable catchment management and planning. To achieve this need, however, we are faced with many challenges including the lack of in-situ DOC measurements at required spatial and temporal scales, and the complex processes controlling DOC confounded by interactions among numerous environmental and climatic factors.

This project will address these challenges by integrating state-of-the-art process-based hydrological-biogeochemical model, satellite remote sensing data, climate change scenarios, and novel machine learning. The aims of this project are to quantify spatial-temporal dynamics of DOC in Swedish river catchments under past and current conditions and future land use and climate scenarios, and to explore environmental variables and processes controlling DOC dynamics.

Work duties
The PhD candidate will carry out research within the funded research project by Oscar och Lili Lamms Minne Foundation. The candidate will be supervised by senior scientists at the department. Main tasks for the PhD candidate include

  • (i) Development and evaluation of hydrological-biogeochemical model for simulating daily DOC concentrations in multiple river catchments
  • (ii) Assessment of impacts of land use and climate changes on DOC concentrations
  • (iii) Investigation of the environmental variables and processes that control the DOC dynamics using a traditional statistical methods and novel machine learning techniques

The main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects and third cycle courses. The work duties can also include teaching and other departmental duties (no more than 20%).

Admission requirements
A person meets the general admission requirements for third-cycle courses and study programmes if he or she:

  • has been awarded a second-cycle qualification, or
  • has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or
  • has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

Qualification requirements

Applicants must have:

  • M.Sc. in Physical Geography, Geomatics, Environmental Science, or a similar topic
  • Good skills in hydrological modelling and programming (e.g. Matlab, Python or R)
  • Documented skills in processing satellite remote sensing data and particularly in land use/cover classification. Proven experience in processing satellite data (particularly Landsat, MODIS, and Sentinel-1/2) is an advantage
  • Good oral and written proficiency in English.
  • Good communication skills and ability to work independently and in a group.
  • Demonstrated experience in analysis of DOC data or DOC modelling.

To be considered for this position, the applicant must include a statement with a detailed motivation to how the above requirements are met, and why the applicant believes that she/he is the right person for the position.

Merits for the position

  • Experience in machine learning and Big Data analytics would be an advantage.
  • Demonstrated experience in writing academic papers in related research fields would be an advantage.

Assessment criteria
Selection for third-cycle studies is based on the student’s potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Special attention is paid to the following:

Knowledge and skills relevant to the thesis project and the subject of study. An assessment of ability to work independently and to formulate and tackle research problems. Written and oral communication skills Other experience relevant to the third-cycle studies, e.g. professional experience.

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant, through his or her experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme.

Terms of employment
Only those admitted to third cycle studies may be appointed to a doctoral studentship. Third cycle studies at LTH consist of full-time studies for 4 years. A doctoral studentship is a fixed-term employment of a maximum of 5 years (including 20% departmental duties). Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

Instructions on how to apply

Applications shall be written in English and include:

  • a statement with a detailed motivation to how the above requirements are met, and why the applicant believes that she/he is the right person for the position.
  • CV,
  • degree certificate or equivalent,
  • other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.).
Type of employment Temporary position
First day of employment as soon as possible or according to agreement
Salary Monthly salary
Number of positions 1
Full-time equivalent 100
City Lund
County Skåne län
Country Sweden
Reference number PA2022/1592
  • Zheng Duan, associate professor,
  • Tina Olsson, human resource coordinator, +46462224853,
Union representative
  • OFR/ST:Fackförbundet ST:s kansli, 046-2229362
  • SACO:Saco-s-rådet vid Lunds universitet, 046-2229364
  • SEKO: Seko Civil, 046-2229366
Published 27.Apr.2022
Last application date 21.May.2022 11:59 PM CEST

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