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to continuation as a researcher at Ericsson Research. Practical work tasks include: Developing algorithms and models for dynamic spectrum sharing using RDT data Implementing and evaluating signal processing and
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Offer Description The successful candidate will be expected to work primarily on aspects related to precision predictions for the LHC and parton-shower simulations. The project encompasses the development
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eigenfrequencies, mode shapes, and modal damping ratios - can indicate structural changes or damage. Along these lines, this project develops SHM methods for deep tunnels, with the HADES Underground Research
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. For additional information, please visit: https://dakic.univie.ac.at/ . Your future tasks: You will actively participate in research, teaching and administration. This means: • You are involved in a well-funded
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1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD position 2 will focus on designing scalable
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interpreted by regression and tree-based machine learning algorithms to obtain even better mutants and develop mechanistic hypotheses. Various collaborations with ON-TRACT network partners across Europe allow a
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to maximise early outbreak detection. Active intervention: developing decision-making algorithms that recommend effective public-health interventions. Reinforcement learning (RL) provides a natural framework
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and data-based models for describing complex materials and (re)active molecules with a focus on their interfaces. Development and implementation of new methodologies and algorithms for simulating
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bottlenecks in clinical radiology workflows through observations, structured workflow mapping, and close collaboration with clinical staff. Design, develop, and evaluate AI-based and automated workflow
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two distinct images from a single PET acquisition. Within this project, we will jointly develop, adapt and implement advanced image reconstruction algorithms in our in-house reconstruction software