Doctoral students in Deep Learning for Automatic Satellite Imagery Analysis - Hiring in process/Finished, not possible to apply

Project description

Third-cycle subject: Computer Science 

The scientific work will focus on developing modern deep learning methods to perform automatic analysis of satellite imagery for earth observation, in particular for change detection in urban areas and those occurring due to monitoring of urban areas. The work will be conducted in the scope of one of the following projects:

• Probabilistic temporal networks for continuous change detection of satellite imagery. In this project, the aim will be to develop probabilistic 2D time series analysis based on deep networks that are able to detect anomalies both temporally and spatially despite natural and regular changes that happen. The immediate applications are in forest fire detection, monitoring urban development, as well as standard computer vision benchmarks.
• Segmentation networks for satellite imagery with better generalization via self-supervised training. The aim will be to develop self-supervised training algorithms that will build powerful representations of visual data from many environments that can encode what normal looks like and is robust to seasonal and weather changes.

The starting date for the positions will be in agreement with the supervisor, but preferably during fall 2020. 

Supervision: The doctoral students will be supervised by Associate Professor Josephine Sullivan and Assistant Professor Hossein Azizpour 

What we offer

• The possibility to study in a dynamic and international research environment in collaboration with industries and prominent universities from all over the world.Read more
• A workplace with many employee benefits and monthly salary according to KTH’s Doctoral student salary agreement.
• Postgraduate education at an institution that is active and supportive in matters pertaining to working conditions, gender equality, and diversity as well as study environment.
• Work and study in Stockholm, close to nature and the water.
• Help to relocate and be settled in Sweden and at KTH.
• Join a vibrant research- and multi-disciplinary group (regular reading groups, supervision, and larger group meetings) with access to good GPU cluster resources.

Eligibility

To be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:

• passed a degree at an advanced level,
• completed course requirements of at least 240 higher education credits, of which at least 60 higher education credits at an advanced level, or
• in any other way acquired within or outside the country acquired essentially equivalent knowledge.
• Requirements for English equivalent to English B/6, read more here.
• The candidate must have a degree in either Computer Science, Engineering, Mathematics, or related fields. Documented written and spoken English and programming skills are required. Experience with modern machine learning and deep learning software packages are desired qualities.

Selection

In order to succeed as a doctoral student at KTH, you need to be goal-oriented and persevering in your work. During the selection process, candidates will be assessed upon their ability to:

• independently pursue his or her work
• collaborate with others,
• have a professional approach and
• analyze and work with complex issues,
• write and communicate orally in English, 
• manipulate and understand modern probabilistic and machine learning methods/models and
• program reliably and accurately with deep learning software packages.

After the qualification requirements, great emphasis will be placed on personal qualities and personal suitability.

Target degree: Doctoral degree

Information regarding admission and employment

Only those admitted to postgraduate education may be employed as a doctoral student. The total length of employment may not be longer than what corresponds to full-time doctoral education in four years ' time. An employed doctoral student can, to a limited extent (maximum 20%), perform certain tasks within their role, e.g. training and administration. A new position as a doctoral student is for a maximum of one year, and then the employment may be renewed for a maximum of two years at a time.

Union representatives

You will find contact information for union representatives on KTH's website.

Doctoral section (Students’ union on KTH Royal Institute of Technology)

You will find contact information for the doctoral section on the section's website.

Application

Apply for the position and admission through KTH's recruitment system. It is the applicant’s responsibility to ensure that the application is complete in accordance with the instructions in the advertisement.

Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/Central European Summer Time).

Applications must include the following elements:

• CV including your relevant professional experience and knowledge.
• Application letter with a brief description of why you want to pursue research studies, about what your academic interests are, and how they relate to your previous studies and future goals. (Maximum 2 pages long)
• Copies of diplomas and grades from previous university studies and certificates of fulfilled language requirements (see above). Translations into English or Swedish if the original document is not issued in one of these languages.
• Representative publications or technical reports. For longer documents, please provide a summary (abstract) and a web link to the full text.

Other information

Gender equality, diversity, and zero tolerance against discrimination and harassment are important aspects of KTH's work with quality as well as core values in our organization.

For information about the processing of personal data in the recruitment process please read here.

We firmly decline all contact with staffing and recruitment agencies and job ad salespersons.

Disclaimer: In case of a discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.