Postdoc of Data Science

Background
At present, field phenotyping of complex traits associated with biomass development is a labor-intensive process, which often involves destructive measurements. Researchers and breeders are therefore interested in incorporating the use of automated, non-destructive, high-through-put field phenotyping methods into pre-breeding and breeding programs. The main advantage of automated systems is the ability of researchers and breeders to easily detect and evaluate dynamic traits, which have so far proven difficult, laborious or even impossible to measure by visual rating or destructive sampling.

The Section for Crop Sciences, Department of Plant and Environmental Sciences (PLEN), Faculty of Science, University of Copenhagen (KU) has stablished advanced platforms for field phenotyping using proximal- and remote sensing at the research station Højbakkegaard, Taastrup. The postdoc will be responsible for carrying out the image analyses of data collected from high-through-put phenotyping platforms at PLEN and contribute to phenotyping research. The position is located at the Department’s experimental farm, Højbakkegaard, at Taastrup Campus. The position will require close collaboration with the plant scientists and technical staff involved in phenotyping projects as well as external collaboration with private breeding companies that are partners to the project. 

The postdoc’s duties will include research within image processing as well as teaching. The post may also include performance of other duties.

Further information about the Department can be found at https://www.science.ku.dk/english/about-the-faculty/organisation/. Inquiries about the position can be made to head of the department, Svend Christensen.

The position is open from April 1, 2023, or as soon as possible thereafter.

The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.

Job description
We are looking for a Data Science researcher with an interest in interdisciplinary research collaborations and a strong foundation in machine learning and image processing. Responsibilities of the position will include the following:

  • Participation in the development, implementation and management of software for high-through-put phenotyping (both image acquisition and analysis).
  • Performing methodological research in image processing, 3D models, point clouds (from LiDAR and/or SFM), vegetation indices and plant morphologies.
  • Preparing drafts and participating as author and co-author for technical documents, relevant scientific papers, conference paper, reports etc. focusing on research related to phenotyping research and activities at the Section for Crop Sciences.
  • Participation in seminars, workgroups and meetings as a technical resource representative of the department.
  • Guiding, training and supervising scientific staff and potentially breeders at private companies as necessary

Education

  • PhD in Agronomy or data science

Competences

  • Professionalism: Demonstrated professional competence and mastery with respect to data science relevant for agricultural research.
  • Ability to conduct independent research and analysis.
  • Ability to identify issues, formulate options/scientific hypothesizes and make conclusions and recommendations.
  • Advanced computer skills and a strong foundation in Machine Learning (ML)
  • Adherence to good data management paradigms
  • Preferably, but not a requirement, a basic understanding of agronomy and plant physiology
  • Is conscientious and efficient in meeting commitments, observing deadlines and achieving results
  • Works collaboratively with colleagues, internal as well as external,  to achieve organizational goals
  • Good communication skills in English, both oral and written
  • Planning & Organizing: Develops clear goals that are consistent with agreed strategies; identifies priority activities and assignments; adjusts priorities as required; allocates appropriate amount of time and resources for completing work; foresees risks and allows for contingencies when planning; monitors and adjusts plans and actions as necessary; uses time efficiently; communicates needed adjustments.

Terms of employment
The position is covered by the Memorandum on Job Structure for Academic Staff.
Terms of appointment and payment accord to the agreement between the Danish Ministry of Taxation and The Danish Confederation of Professional Associations on Academics in the State.
Negotiation for salary supplement is possible.

The application, in English, must be submitted electronically by clicking APPLY NOW below.

The position is a 2 year and 8-month term-limited position.

Please include

  • Curriculum vitae
  • Diplomas (Master and PhD degree or equivalent)
  • Research plan – description of current and future research plans
  • Complete publication list
  • Separate reprints of 3 particularly relevant papers

The deadline for applications is 18  December 2022, 23:59 GMT +1. 

After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee.

You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/.

Interviews will be held in week 3, January 2023.

APPLY NOW

Part of the International Alliance of Research Universities (IARU), and among Europe’s top-ranking universities, the University of Copenhagen promotes research and teaching of the highest international standard. Rich in tradition and modern in outlook, the University gives students and staff the opportunity to cultivate their talent in an ambitious and informal environment. An effective organisation – with good working conditions and a collaborative work culture – creates the ideal framework for a successful academic career.

Contact

Svend Christensen
E-mail: svc@plen.ku.dk

Info

Application deadline: 18-12-2022
Employment start: 01-04-2023
Working hours: Full time
Department/Location: Department of Plant and Environmental Sciences
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