Postdoc in under-supervised deep learning for visual recognition - Hiring in process/Finished, not possible to apply

Job description

Deep networks have been repeatedly proven to benefit from large amount of labelled training data.  Collecting high-quality labelled data, at Google scale, is prohibitively laborious and expensive for most societal bodies and industrial sectors. In this project, the postdoc candidate will work on unifying methods for undersupervised learning which includes semi-supervised, weakly-supervised, and noisily-supervised learning techniques. The postdoc will be hired at Hossein Azizpour’s group at the division of robotics, perception and learning and the project is in collaboration with a twinning postdoc at Aalto university in the group led by Juho Kannala. The postdoc position is funded by WASP (wasp-sweden.org) and is anticipated to involve two concrete applications in medical image analysis and autonomous driving.

What we offer

• A position at a leading technical university that generates knowledge and skills for a sustainable future
• Engaged and ambitious colleagues along with a creative, international and dynamic working environment
• Work in Stockholm, in close proximity to nature
• Help to relocate and be settled in Sweden and at KTH
• Access to a vibrant group of PhD students and postdocs with several related academic activities including reading groups, international seminar series, and joint group meetings.
• Access to state-of-the-art GPU resources (see Berzelius at https://www.nsc.liu.se/systems/berzelius/)
• Access to real-world multi-disciplinary projects including life science and earth observation.
• Financial support for presentation of own and collaborative works at international venues.

Read more about what it is like to work at KTH

Qualifications

Requirements

• A doctoral degree or an equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made.
• Prior research experience with machine learning and computer vision proven with publications in top machine learning and/or computer vision venues.       
• We prefer that you are independent, aware of diversity and equal opportunity issues, and have teaching and collaborative abilities.

Preferred qualifications

• A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
• Independence
• Prior experience with various forms of under-supervised learning will be considered a plus
• Experience of working in larger projects with remote collaborations

Great emphasis will be placed on personal competency.

Trade union representatives

You will find contact information to trade union representatives at KTH's webbpage.

Application

Log into KTH's recruitment system in order to apply to this position. You are the main responsible to ensure that your application is complete according to the ad.

The application must include:

• CV including relevant professional experience and knowledge.
• Copy of diplomas and grades from your previous university studies. Translations into English or Swedish if the original documents have not been issued in any of these languages.
• Brief account of why you want to conduct research, your academic interests, how they relate to your previous studies and future goals, and how they fit to the goal of this position. Max two pages long.

Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).

About the employment

The position offered is for, at the most, two years.

A position as a postdoctoral fellow is a time-limited qualified appointment focusing mainly on research, intended as a first career step after a dissertation.

Others

Striving towards gender equality, diversity and equal conditions is both a question of quality for KTH and a given part of our values.

For information about 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 discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.