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be optimized and quantified using tomographic methods, in particular positron emission tomography, with spatial and temporal resolution. Different types of substrates and contamination are being
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tailored to identify the most effective combination for addressing the challenges posed by the proposed research. This methodological flexibility is crucial for optimizing the model's performance and
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at the nanometer scale. We will use available DNA thermodynamic database, coarse-grained simulations of DNA motifs, and existing experimental data to establish an AI model that is able to guide the construction
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simulate, and eventually manipulate wave propagation under realistic scenarios by intertwining analysis and numerics. The proposed doctoral project concerns the mathematical analysis of dispersive phenomena
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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Description The NHR Graduate School for HPC Talents 9 Ph.D. scholarships for HPC, Numerical Simulations, and AI/ML projects The NHR Graduate School seeks qualified applicants interested in pursuing
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atomistic simulations, high-performance computing, and the application of AI-based methods Basic knowledge in photovoltaics and solid-state materials for energy application Ability to work individually and in
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the MATSim agent-based transport simulation framework. The main task is to enable simulated agents to choose transportation modes, such as car, bus, bike, or walking, based on real-time feedback from
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- and Q-band EPR spectrometer. The candidate will be responsible for designing, building, and optimizing instrumentation and methods to investigate complex catalytic systems — including both synthetic and
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and the effects of disordered correlated microstructures on diffusion; iii) development of energy-based models and numerical simulations of hyperuniform assemblies; iv) development and application