PhD position (f/m/d) Part-time

Research Assistant For Generative AI Methods

We are seeking you to support our research on generative AI methods for improving weather radar data processing. You will be part of the DFG-funded project EuRadCA (Improving rainfall estimation across Europe through advancements in radar data processing, use of crowdsourced data, and AI). Additionally, your work will be embedded into the ongoing research on AI-method developments within the research group Hydrometeorological Sensing and Machine Learning at the KIT Campus Alpin.

The overarching goal of the project EuRadCA is to improve quantitative precipitation estimation (QPE) from weather radar on a European scale with the help of advanced radar data processing, new radar-gauge adjustment techniques, AI methods, and observations from personal weather stations (PWS). The project will be carried out in cooperation with the project partners at the University of Stuttgart, who focus on the PWS data quality control and merging with radar, and partners at TU Delf, who focus on classical radar data processing and a scalable reproducible processing pipeline for Europe-wide data. Your work will focus on the development of new generative AI methods to improve QPE from radar data and from a combination of radar and station data. Additionally, AI-based quality control and an application of the new methods the European OPERA radar network is planned.

Your main tasks will be to:

• Develop a generative artificial neural network (ANN) for probabilistic radar QPE using single- and dual-pol radar data.
• Extend the developed generative ANN to allow additional conditioning on rain gauge data to provide a merged radar-gauge product.
• Develop an AI-based quality control method for radar data
• Apply and evaluate the developed methods on the European scale to data from the OPERA radar network

You have:

• a MSc degree in natural science, geoscience, mathematics, informatics or related fields.
• strong programming skills, preferably in Python;
• basics knowledge of deep learning, preferably in pytorch;
• expertise with handling large datasets and machine learning workflows;
• basic knowledge of meteorology and remote sensing; and the intuition to link data and code to real-world observations and challenges;
• a good command of written and spoken English.

We offer:

• Science for Impact: Engage with topics of societal relevance — in an excellent scientific environment that enables change.
• Flexible Working Hours: Take advantage of flexible‑hours schemes, remote‑work options, part‑time models, and a 30‑day annual leave entitlement to achieve an optimal work‑life balance.
• Career‑Building and Developmental Opportunities: We provide you with a structured onboarding program, a broad spectrum of continuing‑education options, and personalised support, thereby fostering your individual growth.
• Family-friendliness: The “KIT‑Family +” program assists you in reconciling work and family life by offering childcare services, holiday activities, a parent‑child office space, and assistance with caring for relatives.
• Stay Healthy: Under the motto “Fit at KIT – Body, Mind and Soul,” we promote your well‑being through fitness classes, mental‑health programmes, and regular preventive health examinations.
• Individualised Extra Benefits: Enjoy a corporate pension (VBL), a €25 monthly contribution toward a JobTicket BW, plus a broad selection of cultural and recreational programmes.

Job location Garmisch-Partenkirchen

Salary Salary category 13 TV-L; classification is based on personal and professional qualifications.

Contract duration 3 years

Contact person in line-management Dr. Christian Chwala christian.chwala@kit.edu

If you have general questions about the application process, please contact Kristin Rink Personalservice (PSE) kristin.rink@kit.edu +49 721 608-25004

At KIT we value the diversity of our employees; different perspectives and backgrounds enrich our work. We therefore welcome applications from all candidates. Women are especially encouraged to apply. Applications from recognized severely disabled individuals are given preferential consideration when qualifications are equal. Application up to: 2026-07-06 Job posting number: 207/2026