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Course paper/final thesis
Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our 11,000 employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge? Then this is your place!
For our Institue of Software Technology in Sankt Augustin we are looking for a
Student (f/m/x) Computer Science or similar
Design and implemenation of algorithms to assess the role of nodes in spreading processes on networks
What to expect:
In this thesis, you will delve into the challenging problem of identifying vital nodes in complex, temporal networks. Vital nodes are those with the most influence on the network, which is critical in various scenarios, such as the spread of viruses, misinformation, and radicalization. Our focus is on epidemic preparedness and controlling virus spread. However, the methods you develop could be adapted to other fields as well.
Your task will be to design and implement a novel algorithm that combines message-passing principles with machine learning techniques in temporal networks. The unique aspect of this work is the use of privacy-preserving methodologies that do not require direct access to the entire network. Instead, you will work with indirectly propagated information to estimate a node's vitality.
Your task will be to design and implement a novel algorithm that combines message-passing principles with machine learning techniques in temporal networks. The unique aspect of this work is the use of privacy-preserving methodologies that do not require direct access to the entire network. Instead, you will work with indirectly propagated information to estimate a node's vitality.
Key areas you will explore include:
- temporal networks and their dynamics
- machine learning and graph neural network methods and their applications in complex networks
- privacy-preserving machine learning approaches
- infection risk modeling and epidemic preparedness
You will have the opportunity to build on state-of-the-art methods and extend them to new, more robust, and scalable approaches.
What we expect from you:
- pursuing a Master’s degree in Computer Science or a related field
- strong interest in machine learning, complex networks, and potentially graph neural networks
- solid programming skills (e.g., Python, TensorFlow, PyTorch)
- ability to work independently and systematically on complex research problems
What 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.
Further information:
Starting date: sofort
Duration of contract: 6 months
Type of employment: Full-time
Duration of contract: 6 months
Type of employment: Full-time
Remuneration: Je nach Qualifikation und Aufgabenübertragung bis Entgeltgruppe 05 TVöD
Vacancy-ID: 98302
Contact:
Diaoule Diallo Institut für Softwaretechnologie
Tel.: 02203 601 1276
Diaoule Diallo Institut für Softwaretechnologie
Tel.: 02203 601 1276
