Postdoc in Knowledge Graph for Urban Transportation - Hiring in process/Finished, not possible to apply

Job description

The commitment to decarbonization and positive energy district is driving cities and service providers to leverage emerging techniques to promote sustainability that could improve or innovate conventional practices in all fields. The ubiquitous sensor data and research findings from literature/reports provide invaluable knowledge driving decision makings. However, the question is how to synthesize and interpret the vast evidence effectively, efficiently, and timely, given the diversity of research topics, inconsistency and sparse findings, and the large volume of evidence accumulation.

We are looking for a highly qualified and motivated postdoc in getting hands-on research experience in the area of Transportation Science, Knowledge Graph, and Machine Learning/AI (e.g., large language models). The postdoc will join a research team at KTH led by Dr. Zhenliang Ma. The postdoc is expected to develop the factual knowledge graph for urban transportation decision makings from structured and unstructured data, works closely with the researchers specializing in transportation science and computer science.

Supervision: The project is funded by the Swedish Strategic Research Area in Transportation. The postdoc will be supervised by Dr. Zhenliang Ma.

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

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.
• A strong research expertise in knowledge graph, transportation, and machine learning/AI or related disciplines.
• Previous experience with large data analytics, knowledge graph, large language models, and Python programming using deep learning architectures.
• Theoretical background in machine learning and transportation engineering.

Preferred qualifications

• A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
• Teaching capabilities in applied artificial intelligence in transportation.
• Awareness of diversity and equal opportunity issues, with specific focus on gender equality
• Candidates applying for this position are comfortable working in groups as well as independently, and they have a good sense of structure in their daily work tasks.

Great emphasis will be placed on personal skills.

Trade union representatives

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

To apply for the position

Log into KTH's recruitment system in order to apply for 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.
• Brief account of your academic interests, why you are interested in the research topic, and how could you contribute to the topic if selected. 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

The position may include security-sensitive activities. To become authorized, you therefore need to pass a security check.

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.

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