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simulations (in collaboration with a postdoctoral researcher) Evaluation of localisation results with regard to physical plausibility and accelerator operation Visualisation of extracted parameters and
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Your Job: You will be part of a research team that applies high-throughput experimentation to accelerate research in the emerging field of electrocatalysis. Catalyst inks are at the forefront
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: Investigate and design optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical
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, graph neural networks, physics-informed ML) to approximate PF results Train models using simulation results generated from conventional power flow solvers Evaluate AI-based approximators in terms
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particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design
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at: https://www.ipb-halle.de/institut/ Data protection: Please note the data protection information for applicants (m/f/d) in accordance with Art. 13 and 14 GDPR on data processing in the application process
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, accelerate global biodiversity discovery through open museum data, and unravel the evolutionary history of Annelida – a diverse, ecologically important, and globally distributed but still understudied animal
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to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design‑space exploration, and on‑line operational optimization of power systems
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, able to target HPC and the above novel architectures Your Profile: Master’s degree (preferably with subsequent PhD degree) in physics or a related field at the start date (ideally with a background in
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular