Geospatial Data Researcher

Geospatial Data Researcher – Digital Twin Lab To support our motivated team in the Digital Twin Lab research group, we are looking to fill the following position with immediate effect based in Klagenfurt am Wörthersee.

The Digital Twin Lab researches and applies automated methods for the creation and use of highly accurate spatial digital twins of real environments.

These digital images enable realistic simulations, for example to investigate the effects of climate change, evaluate infrastructure, or analyze traffic‑related issues, whereby a practically unlimited number of scenarios can be tested safely and without complex real test setups.

In addition, the data collected – in combination with links to real‑world sensors and, where appropriate, real‑time data – serves as a ground truth basis for the development and validation of simulation models and automated mobility solutions, from high‑precision map material and the validation of vehicle systems to the evaluation of infrastructure measures and the further development of safe, efficient mobility concepts.

This work is based on state‑of‑the‑art mobile mapping equipment (vehicle‑, drone‑ and person‑guided), high‑performance computing infrastructures and largely automated data processing.

Based on AI‑supported evaluation methods, potential analyses are developed, e. g. for the greening of urban areas, as well as impact analyses using immersive environments – e. g. to investigate the acceptance of automated mobility solutions.

These environments can be used with XR technologies, such as VR glasses or driving simulators, to make the planning and analysis processes of measures a vivid experience.

Job Details Immediate start Unlimited term Full time Gross monthly salary €4,056 (payable 14 times per year).

Salary may be adjusted in accordance with qualifications and experience.

Responsibilities and Qualification Development and application of methods for the automated analysis of geodata, in particular 3D point clouds and image data from mobile mapping.

Use of modern computer vision and machine learning methods for object recognition and classification (e. g. terrain, vegetation, road surface properties).

Participation in research and development projects in the fields of mobility, infrastructure and environment.

Coordination and management of tasks within interdisciplinary project teams.

Preparation, documentation and presentation of results for project partners, specialist audiences and funding bodies.

Degree in geoinformatics, computer science, telematics, robotics, game engineering, surveying or a comparable field of study.

Experience in handling and developing methods of data analysis, computer vision or machine learning (e. g. convolutional neural networks, Point Net or segmentation algorithms).

Programming skills (e. g.

Python) for the development of analysis workflows.

Interest in the processing of 3D geodata (mobile mapping, laser scanning, photogrammetry, …).

Strong communication and presentation skills in German (Level C1) and English.

Advantageous: knowledge of common geodata tools (e. g.

PDAL, GDAL, QGIS), experience with relevant data formats, standards or know‑how of road construction parameters.

What We Offer Fair and market‑aligned salary. €4,056 gross per month, payable 14 times per year (pro‑rata), based on the collective bargaining agreement for employees in non‑university research – with a competitive salary above the agreement a given depending on professional experience and qualifications.

Team‑oriented working environment and state‑of‑the‑art infrastructure.

Flexible working hours (flexitime without core hours) with the option of teleworking.

Targeted support for your personal career through the internal training program and further development of your expertise.

Opportunity to combine your professional research activities with a dissertation.

Additional full days off on Good Friday, 24 Dec. and 31 Dec.

Sports and cultural promotion, mobility allowance, company events and social benefits. #J-18808-Ljbffr