Doctoral student in Causal Representation Learning

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.

Project description

Third-cycle subject: Computer science

Deep neural networks have achieved outstanding success on prediction tasks in a supervised learning setting when enough labeled data is available. Yet, current AI systems are limited in their ability to understand the world around us, as shown in a limited ability to transfer and generalize between tasks.

In order to achieve the goal of designing and understanding intelligent machines, a key requirement is the ability to build latent generative models and learn from interventions & counterfactuals. This corresponds to an interactive learning environment, where the agent can discover causal factors through interventions, observe their effects and assign credit to each of its actions.

The goal of this position is thus to investigate how one can learn causal models of data from a reasonable set of assumptions as well as the experimental design of performing interventions and acquiring interventional data efficiently.

In this project, you will be involved in state-of-the-art machine learning research from the beginning and provide you an opportunity to get started immediately with your topic. Our group is one of the few worldwide which successfully combines causality and deep learning with downstream applications from robotics to healthcare.

Our research is driven by real-world challenges with the potential for high societal impact and our work covers a wide spectrum between theory and application, with plenty of flexibility to adjust projects to your preferences. However either a very strong theoretical background or experience with the common deep learning frameworks/software engineering skills is required.

Supervision: The doctoral student will be supervised by Bo Wahlberg & Stefan Bauer

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.
• A 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.

Admission requirements

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 second cycle degree (for example a master’s degree),
• completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or
• acquired, in some other way within or outside the country, substantially equivalent knowledge
• Experience in Deep Learning or strong background in Statistics are required

In addition to the above, there is also a mandatory requirement for English equivalent to English B/6, read more here

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.

After the qualification requirements, great emphasis will be placed on personal competency.

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 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

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.

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