Stellenticket Technische Universität Dresden

Within the framework of the DFG-funded project "Cart2Former – Generative Transformer Models for the Cartographic Generalization of Vector Data Incorporating Spatial-Semantic Context Information", you will conduct research on AI methods for the cartographic generalization of geographic vector data.

• Independent scientific work within the Cart2Former project
• Conceptualization, development, and implementation of novel transformer-based model architectures for geographic vector data
• Creation and preparation of training and benchmark datasets from heterogeneous geospatial data sources
• Training, evaluation, and benchmarking of the developed AI models
• Publication and presentation of research results at international conferences and in scientific journals
• Preparation of project reports and final reports

• Successfully completed university degree (Master, Diplom, or equivalent) in Geodesy/Geoinformatics, Computer Science, Data Science, or a related field
• Experience in the field of AI and deep learning, particularly with neural networks
• Proficiency in deep learning frameworks such as PyTorch or TensorFlow
• Excellent programming skills in Python
• Solid knowledge of the processing and analysis of geospatial data
• Basic knowledge of cartography, particularly cartographic generalization, is an advantage
• Ability to conduct independent scientific research desirable
• Good skills in German and/or English language in speech and writing required; willingness to learn either English or German is expected

Please send your application with the reference number and the usual documents only by email (single pdf file, max. 5 MB) to Prof. Dr. Kada to martin.kada@tu-berlin.de.

By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guaranty for the protection of your personal data when submitted as unprotected file. Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at the TU staff department homepage: https://www.abt2-t.tu-berlin.de/menue/themen_a_z/datenschutzerklaerung/.

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities. Applications from people of all nationalities and with a migration background are very welcome.