Master thesis (f/m/x): AI-Based 3D Reconstruction of Atmospheric States for Nowcasting Applications

German Aerospace Center (DLR); Almeria; On-site; PHP 471,000 - 785,000; Full time

Job summary

German Aerospace Center (DLR) invites Master’s students to join a research project in Almería, Spain, to work on atmospheric reconstruction and 3D representations using multi-modal data. You will explore self-supervised learning, physics-informed modeling, and 3D reconstruction techniques within an international team.

The position runs from 4 January 2027 for six months full-time as an Erasmus+ guided thesis with hands-on ML and software development work.

Qualifications

• Strong academic record in a master program in computer science, physics, mathematics, engineering or related field.
• Experience in Python and basic knowledge about machine learning.
• Ability to work independently and collaborate in an international team.
• Prior experience in data analysis, computer vision and git versioning systems.
• Confident in speaking and writing English.

Responsibilities

• Get familiar with existing methods for 3D reconstruction and machine learning
• Analyze and preprocess multi-modal atmospheric observation data
• Implement and test a 3D atmospheric reconstruction model using modern deep learning tools
• Improve model performance by incorporating physical constraints and consistency between different data sources
• Analyze and visualize the reconstructed 3D atmospheric fields and their uncertainty
• Document methodology and results in a well-structured Master’s thesis

Skills

Python

Machine learning basics

Data analysis

Git versioning

Independent working

English communication

Education

Master's in CS/Physics/Math/Engineering

Tools

Git

Job description

Vacancy-ID: 6877

Place of work: Almeria

Starting date: 04.01.2027

Career level: Student research project and final thesis

Type of employment: Full-time

Duration of contract: 6 Monate

Remuneration: Allowance with ERASMUS+ grant

The Institute of Solar Research develops innovative technologies for the utilizatioTVöD-Bund).n of solar energy. The focus is on electricity generation and the provision of heat and fuels. The primary goal is to use solar energy to contribute to the heat transition and a reduction in fossil fuels.

What To Expect

This master's thesis explores the challenge of creating physically interpretable representations of atmospheric states using sparse, multimodal observations. It explores learning-based approaches for continuous three-dimensional representation and reconstruction, such as neural field representations and probabilistic generative models, to infer continuous atmospheric variables from heterogeneous data sources, including all-sky imager observations, satellite imagery, and weather radar measurements. The emphasis is on self-supervised learning using geometric, temporal, and physical consistency constraints to enable 3D reconstruction without direct ground-truth volumes. This study aims to advance uncertainty-aware representation learning for underdetermined atmospheric imaging problems and contribute to physics-informed generative modelling in geophysical systems.

You will be part of a diverse and motivated team working on energy-transition topics and contributing to climate protection. Close collaboration with supervisors and colleagues will support you in exchanging ideas and solving challenges. You will gain hands-on experience in machine learning, software development, automated testing, version control and modern image-processing technologies. A particular highlight of the project is the opportunity to work in Almería, Spain, one of the sunniest locations in Europe.

Your Tasks

• Get familiar with existing methods for 3D reconstruction and machine learning


• Analyze and preprocess multi-modal atmospheric observation data


• Implement and test a 3D atmospheric reconstruction model using modern deep learning tools


• Improve model performance by incorporating physical constraints and consistency between different data sources


• Analyze and visualize the reconstructed 3D atmospheric fields and their uncertainty


• Document methodology and results in a well-structured Master’s thesis

Your Profile

• You have a strong academic record in a master's program in computer science, physics, mathematics engineering or a related field


• Experience in Python and basic knowledge about machine learning


• The ability to work independently and collaborate in an international team


• Prior experience in data analysis, computer vision and git versioning systems


• Confident in speaking and writing English

We offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 6877) please contact:

Yann Fabel ([e-mail])

Dr. Stefan Wilbert

Tel.: [phone]