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Job ID: 143202

Early Stage Researcher (PhD student), Machine Learning and Inverse Problems in Remote Sensing
University of Eastern Finland


Date Posted Jun. 23, 2020
Title Early Stage Researcher (PhD student), Machine Learning and Inverse Problems in Remote Sensing
University University of Eastern Finland
Kuopio, Eastern Finland, Finland
Department Department of Applied Physics
Application Deadline Jul. 31, 2020
Position Start Date Sep. 1, 2020
 
 
  • Graduate Student
  • Statistics & Actuarial Science
    Physics - General
    Mathematics/Applied Mathematics
    Computer/Information Sciences
    Atmospheric Sciences
 
 
The University of Eastern Finland, UEF, is one of the largest multidisciplinary universities in Finland. We offer education in nearly one hundred major subjects and are home to approximately 15,500 students and 2,500 members of staff. We operate in Joensuu and Kuopio. In international rankings, we are ranked among the leading universities in the world. The Faculty of Science and Forestry operates on the Kuopio and Joensuu campuses of the University of Eastern Finland. The mission of the faculty is to carry out internationally recognised scientific research and to offer research-education in the fields of natural sciences and forest sciences. The faculty invests in all of the strategic research areas of the university. The faculty’s environments for research and learning are international, modern and multidisciplinary. The faculty has approximately 3,800 Bachelor’s and Master’s degree students and some 490 postgraduate students. The number of staff amounts to 560. http://www.uef.fi/en/lumet/etusivu

We are now inviting applications for an Early Stage Researcher (PhD student) position, Department of Applied Physics, Kuopio Campus
We are seeking a highly motivated Early Stage Researcher (PhD student) to join the project “Post-process correction of satellite data products with a new machine learning based approach”, carried out jointly with the Finnish Meteorological Institute and funded by the European Space Agency (ESA). The aim in the project is to develop a machine learning approach for correction of the approximation errors in the operational satellite retrieval algorithms. The PhD project consists of development and computational implementation of the machine learning models and validation of them using earth observation data. In particular, the aerosol optical depth (AOD) and Angstrom exponent (AE) data from the Sentinel-3 SYNERGY aerosol data products are studied as our test cases.

The person to be appointed to this position will work in the Computational Physics and Inverse Problems research group, see https://uefconnect.uef.fi/en/group/computational-physics-and-inverse-problems-research-group/ for more details. The group is affiliated with the Centre of Excellence of Inverse Modeling and Imaging (Academy of Finland). The candidate should possess strong programming skills (e.g. MatLab, Python) and she or he should preferably have background in physics, statistics, computer science, engineering or applied mathematics. Previous experience in machine learning and related libraries such as Tensorflow, remote sensing, inverse problems or statistics is beneficial. A person to be appointed as an early stage researcher shall be qualified to pursue postgraduate studies, i.e. he or she is required to have completed a suitable higher university degree or an equivalent education abroad, which in the country in question would qualify the person for similar academic studies (UEF University Regulations, Section 31).

English will be used as the language of instruction and supervision in this position.

The position will be filled initially for a fixed term of one year from 1. September 2020, or as agreed. The maximum duration of a PhD position is four years. Positions of early stage researchers shall always be filled for a fixed term (UEF University Regulations, Section 31).

The salary of the position is determined in accordance with the salary system of Finnish universities and is based on level 2 - 4 of the job requirement level chart for teaching and research staff. In addition to the job requirement component, the salary includes a personal performance component, which may be a maximum of 50 % of the job requirement component. The salary is typically between € 2,360.00 - € 3,095.00/month. The salary is determined in accordance with the phase of the doctoral studies and personal performance.

Finland provides one of the most advanced and comprehensive social security in the world. It includes for example sickness benefits, various family benefits and a comprehensive healthcare system. Please read more about working at the University of Eastern Finland and living in Finland.

For further information on the position, please contact: Professor Ville Kolehmainen, tel. +358 40 355 2054, e-mail: ville.kolehmainenuef.fi. For further information on the application procedure, please contact: Executive Assistant Satu Turunen, e-mail satu.turunenuef.fi, tel. +358 29 445 3031.

A probationary period is applied to all new members of the staff.

The electronic application should contain the following appendices:
- Motivation letter
- Curriculum vitae
- a list of publications
- copies of the applicant's academic degree certificates/diplomas, and copies of certificates / diplomas relating to the applicant’s language proficiency, if not indicated in the academic degree certificates/diplomas

The application needs to be submitted no later than 31 July 2020 (by 24.00 hours Finnish time) by using the electronic application form.
For call of application & link for electronic applications, see: https://www3.uef.fi/en/uef/en-open-positions


 
Please reference AcademicKeys.com in your cover letter when
applying for or inquiring about this job announcement.
 
 

Contact Information

 
  • Ville Kolehmainen
    Department of Applied Physics
    University of Eastern Finland
    Registry Office, University of Eastern Finland
    P.O.Box 1627
    Kuopio, Eastern Finland
    Finland
  •  
  • +358 40 355 2054
  • Ville.Kolehmainen@uef.fi

 

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