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interdisciplinary and international team of computational and experimental scientists, gaining unique opportunities to develop novel computational tools and pipelines for the analysis and integration of genomic
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offers first-class cooperation and development opportunities. In section 1.2 Global Geomonitoring and Gravity Field (Department „Geodesy“), we are looking for two positions: Reference Number 10837
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learning algorithms (e.g., graph neural network (GNN) architectures) will be developed to explain the identified small-scale processes as accurately and efficiently as possible and to ultimately develop a
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: Develop an event-driven RL algorithm that sparsely updates network state and parameters that will significantly improve energy to-solution efficiency compared to conventional digital accelerators when
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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the group of Prof. Dr. Barbara Wohlmuth is a project-independent research position without teaching obligations. It offers the opportunity to pursue project-independent research in one of the group’s numerous
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artificial intelligence (AI) algorithms contributing, administrating and reporting in (inter-)national research and development projects presenting results at international conferences close cooperation with
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and development in designing and programming fied programmable gate arrays (FPGAs) for accelerating artificial intelligence (AI) algorithms. contributing, administrating and reporting in (inter
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the acceleration of relativistic plasma in jets. Developments of new automated algorithms for VLBI model-fitting, kinematics measurements and robustness assessment. 2. Probing the physical mechanism of neutrino
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multi-parameter ion-beam tuning procedures (collaboration with Univ. of Vienna and HZDR) and developments of machine learning (ML)-algorithms for optimization of beam parameters and control of relevant