GFZ is Germany's national centre for solid Earth research. We advance the understanding of dynamic processes to address global challenges, from mitigating the impacts of natural hazards and sustaining our habitat amid global change to responsibly managing georesources. We are part of the Helmholtz Association, the largest German scientific organisation. With around 1,200 employees as well as ca. 500 guest researchers, we contribute to the Helmholtz Research Field Earth and Environment, aligning cutting-edge research with societal relevance and international collaboration. Our work integrates multidisciplinary studies across Earth's system components, leveraging advanced technologies and infrastructure to research solutions and to transfer our knowledge to society. We are doing this according to our vision: "Taking the pulse of our Earth to safeguard a habitable planet”.

Reference Number 11078

Global climate change disproportionately affects the Arctic region, carrying the potential to accelerate multiple positive climate feedbacks. Land-surface processes link the feedback between global climate and Arctic conditions. These processes sculpt landscapes and ecosystems underlain by permafrost, impacting areas as far as the Arctic Ocean. High-resolution remote sensing data is essential for studying surface processes in the Arctic region. We are seeking a candidate to build and maintain our data pipeline and apply it to several of our existing machine learning models, especially those dealing with super-resolution problems. The position will be supervised by Dr. Hui Tang from the Earth Surface Process Modelling section at the GFZ. The successful applicant will also collaborate with partners from the Helmholtz Centre for Polar and Marine Research (AWI).

Your responsibilities:

• Develop and use coupled deep learning models to interpret in situ and remote sensing data from Arctic regions
• Testing and verifying the diffusion model to study given areas
• Further developing the models as needed to test and validate concepts and scenarios
• Collaborate with other project members from AWI
• Interact with other members of the Earth Surface Process Modelling Section at weekly group meetings and informally
• Present results at scientific meetings and in peer-reviewed journal publications
• Supervise PhD and Master's students

Your qualifications:

• A Master`s degree (or equivalent) and a PhD in Earth Sciences, Physics, or Mathematics, demonstrating excellence in guided research
• Research experience in machine learning, computer vision, and diffusion models
• Experience of working with PyTorch, TensorFlow, Keras, Pandas, and Xarray
• Ability to use programming languages such as Python, MATLAB, C, C++, or Fortran
• Experience in developing scientific data pipelines
• Proven ability to work in a collaborative and multidisciplinary environment
• Creativity and critical thinking skills, as well as the ability to publish original and innovative work (preprints will be considered)
• Capacity for international teamwork and excellent communication skills
• Proficiency in spoken and written English

What we offer:

• Ambitious and varied tasks in a dynamic and international research environment
• State-of-the-art equipment
• Public service benefits
• Extensive training opportunities
• Professional career advice offered by our in-house Career-Centre
• Flexible working hours and conditions
• Support with finding a good work-life balance offered by benefit@work
• Institute day-care centre on site
• Working at the Albert Einstein Science Park on the Telegrafenberg in Potsdam
• Workplace within walking distance of Potsdam main train station, or just a short ride on the shuttle bus

Start date: As soon as possible
Fixed-term: 6 months
Salary: The position is classed as salary group 13 according to "TVöD Bund (Tarifgebiet Ost)”. The salary group is determined on the basis of the Collective Wage Agreement and the respective personal qualifications.
Working hours: Full-time (currently 39 h/week); The position is generally suitable for part-time work.

Place of work: Potsdam