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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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an extensive safety analysis and calidation of perception algorithms in automotive. Through our work, we lay the foundation for a reliable digital future. What you will do Generative AI opens up new
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Your Job: In this Master’s thesis, you will investigate the impact of different programming algorithms on the stability of resistance states using a sophisticated 3D Kinetic Monte Carlo (KMC) model
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- conducting processors with respect to practical short-depth (NISQ) quantum algorithms Cooperate and actively work with experimental partners developing quantum processors using these technological platforms
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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starting date is November 2025. The topic of the PhD project will be theoretical research in discrete optimization, with a particular focus on either graph algorithms or multiobjective optimization
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dynamics or Hamiltonian eigenstates with classical and quantum algorithms Comparison of quantum algorithms with classical baselines Analysis and documentation of results What you bring to the table Student
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the environment, including traffic conditions, travel time, and cost. The project will define the DRL components (states, actions, rewards, policies), select and implement suitable DRL algorithms, and integrate
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implementation, practical application, theoretical analysis and evaluation of AI algorithms Use of XAI tools to explain machine learning models Implementation of deep learning Improvement of models, e.g. in terms