The Machine Learning Section at the Department of Computer Science (DIKU) invites applicants for two fully-funded PhD fellowships in Theory and Application of Deep Learning for Remote Sensing Data and Predictive Modelling of Ecosystems.
Start date is expected to be December 1, 2022, or as soon as possible thereafter.
The project
The research will be conducted in the Machine Learning Section at the Department of Computer Science (DIKU) in collaboration with the Department of Geosciences and Natural Resource Management. The PhD candidates will join a team developing and analyzing machine learning algorithms that help in achieving sustainable development goals. The work will be part of the research projects DeReEco: Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics (https://ai.ku.dk/research/research-projects/dereeco/) and Risk-assessment of Vector-borne Diseases Based on Deep Learning and Remote Sensing (https://novonordiskfonden.dk/en/news/research-in-denmark-will-protect-cities-in-africa-against-malaria-mosquitoes/).
The goal of the announced PhD fellowships is to combine machine learning, remote sensing, and physical modelling to monitor, predict, and simulate changes in ecosystem properties – such as human settlement structures, agricultural use, livestock, tree and forest cover, water bodies, and carbon stocks.
- The first PhD will focus on novel deep learning algorithms for the analysis of satellite- and drone-based remote sensing data (images and 3D point clouds).
- The second PhD will focus on combining physical modelling, machine learning (in particular deep learning), and efficient computation to develop models of ecosystems that allow for high spatiotemporal accuracy.
When applying, the cover letter should indicate if the applicant has a preference for one of the two PhD topics.
Principal supervisor is ELLIS Fellow Professor Christian Igel, Department of Computer Science, igel@di.ku.dk. The projects will be co-supervised by Professor Rasmus Fensholt, Department of Geosciences and Natural Resource Management.
Who are we looking for?
We are looking for candidates within the field(s) of machine learning (ML). Applicants should be interested in basic research in ML as well as applications of ML to the analysis of ecosystems. Applicants can have a background in computer science/engineering and applied mathematics/statistics. Experience in working with imaging data, in particular from remote sensing, is a plus.
Our group and research- and what do we offer?
The University of Copenhagen is consistently listed as one of the top universities in Europe. Within computer science, it is ranked second in the European Union according to the Academic Ranking of World Universities (ARWU) 2021. The Department of Computer Science, founded by Turing award winner Peter Naur, offers a friendly and thriving international research and working environment. The Machine Learning Section is closely linked to the ELLIS Unit Copenhagen (https://ellis.eu/) and the Pioneer Center for Artificial Intelligence (https://di.ku.dk/ai-centre/).
Copenhagen is one of the 10 most livable cities in the world with a rich culture within music, theater and associations. Life for families is made easy by a publicly supported daycare and health care system, dual career opportunities, maternity/parental leave and six weeks of paid annual vacation. International candidates may find information on living and working in Denmark here. Useful information is also available at The International Staff Mobility office (ISM) at the University of Copenhagen (link). ISM offers a variety of services to international researchers coming to and working at the University of Copenhagen.
The PhD programme
The PhD programme is a three year full-time study within the framework of the regular PhD programme (5+3 scheme).
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Qualifications needed for the regular programme
To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. computer science. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.
Employment as PhD fellow is full-time and for a maximum of 3 years.
Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.
The terms of employment and salary are in accordance with the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure.
Responsibilities and tasks in the PhD programme
- Carry through an independent research project under supervision
- Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE
- Participate in active research environments, including a stay at another research institution, preferably abroad
- Teaching and knowledge dissemination activities
- Write scientific papers aimed at high-impact journals
- Write and defend a PhD thesis on the basis of your project
We are looking for the following qualifications:
- Professional qualifications relevant to the PhD project
- Relevant publications
- Relevant work experience
- Other relevant professional activities
- Curious mind-set with a strong interest in basic research and in developing ML methods that help in achieving the sustainable development goals
- Good language skills
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Application and Assessment Procedure
Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.
Please include:
- Motivated letter of application (max. one page), stating if you have a preference for one of the two PhD topics
- Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position
- Original diplomas for Bachelor of Science or Master of Science and transcript of records in the original language, including an authorized English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted.
- Publication list (if possible)
- Reference letters (if available)
Application deadline:
The deadline for applications is September 1, 2022, 23:59 GMT +2.
We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.
The further process
After deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified, whether you will be passed for assessment.
The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/.
Interviews with selected candidates are expected to be held in week 37.
Questions
For specific information about the PhD fellowship, please contact the principal supervisor.
General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website: https://www.science.ku.dk/phd/.
The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position.
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.
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