Research Associate (m/f/d) in the area of Uncertainty Quantification at Technische Universitt Mnchen
80333 München, , Germany -
Full Time


Start Date

Immediate

Expiry Date

24 Oct, 25

Salary

0.0

Posted On

24 Jul, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, Python, Uncertainty Quantification, Physics, Geoinformatics, Software Development, Statistics, Deep Learning, Computer Science, Mathematics

Industry

Information Technology/IT

Description

We are looking for a Research Associate (m/f/d) in the area of Uncertainty Quantification effective January 1st, 2022. The Chair of Remote Sensing Technology is a joint appointment of the Technical University of Munich and the Remote Sensing Technology Institute of the German Aerospace Center (DLR). It engages with the development and fundamental research of computer vision and machine learning methods for automated analysis and processing of multimodal remote sensing data.

ABOUT US

The Technical University of Munich (TUM) is committed to excellence in research and teaching, interdisciplinary education, and the active promotion of promising young scientists. TUM also forges strong links with companies and scientific institutions across the world and regularly ranks among the best European universities in international rankings. The newly founded Faculty of Aerospace and Geodesy plays a central role within the high-tech agenda of the Bavarian State Government and will address current issues of urban mobility, digitization, and environmental protection and advance them with modern, strongly international, and interdisciplinary approaches in research and teaching. In cooperation with established industrial and research institutions, an internationally competitive “Space Valley” is to be created in the Munich Metropolitan Region.

REQUIREMENTS

We are searching for excellently-qualified graduates with a master’s degree in computer science, electrical engineering, data sciences, geoinformatics, statistics, mathematics, physics or comparable. Further, the successful candidate will show

  • in-depth methodological and applied knowledge in the field of machine learning, especially deep learning,
  • experiences in the area of uncertainty quantification, generative and Bayesian deep learning
  • practical experience with software development in Python and common deep learning frameworks and libraries,
  • experience in handling large-scale data stocks, ideally geodata,
  • analytical thinking, independent and structured work, as well as willingness to cooperate with other team members, and
  • good English language skills.
Responsibilities

You will take over research and development tasks in the BMWi-funded project “DUKE” in cooperation with the Max Planck Institute for Biogeochemistry located in Jena. Find out more on https://www.asg.ed.tum.de/lmf/duke.

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