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analysis and statistical methods Can think out of the box, distinguish main lines from details, and provide structure to their work Excellent working and communication skills in research & clinical
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Harnessing Nonlinear Dynamics: From Data-Driven Discovery to Engineering Job description Nonlinear dynamics lies at the centre of many mechanical systems, from large-scale structures to nanoscale
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, or related discipline; A relevant background in fluid mechanics and experimental methods; Hands-on experience with modern experimental methods for flow measurement is preferable; Affinity for working with
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experimental data, enabling accurate prediction of nonlinear phenomena such as modal interactions and dissipation pathways across scales: from complex structural assemblies to nanomechanical resonators. In
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candidate with: A Master’s degree (or equivalent) in mechanical engineering, applied physics, or related discipline; A relevant background in fluid mechanics and experimental methods; Hands-on experience with
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interact and communicate — insights that are essential for understanding the cellular origins of brain disorders. Despite major progress in genetics research, we still don’t know how genetic risk factors for
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on construction and performance indicators. This will require an insight into how to fully automate the discrete topology design synthesis process across system levels and how to learn component descriptions and
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a relevant field (e.g., transportation, human-computer interaction, architecture, cognitive science, or related disciplines). Experience with XR technologies, including development in Unity or Unreal
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mucin-hydrolase activity in bacterial secretomes and patient samples. A successful candidate will gain training in chemical biology, enzyme biochemistry, structural biology and probe development, while
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for donor kidneys. Central to this is the use of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies