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are available in the Centre of Infrastructural Monitoring and Protection (CIMP), School of Civil and Mechanical Engineering at Curtin University, Australia. The successful candidates will work with Associate
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19 Mar 2026 Job Information Organisation/Company Senckenberg Gesellschaft fuer Naturforschung Department Department of Molecular Ecology at the Senckenberg Biodiversity and Climate Research Centre
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experts in the prognostics and condition monitoring field, as well as being part of our strong and dynamic research centre at Cranfield University. About the host University/Centre Cranfield is an
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 27 days ago
: Large-Space Gaze Analysis via Multi-camera System for Social Interactions” to develop datasets and algorithms to capture and analyze eye gaze. About the project We are supporting the development of highly
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19 Mar 2026 Job Information Organisation/Company Senckenberg Gesellschaft fuer Naturforschung Department Department of Molecular Ecology at the Senckenberg Biodiversity and Climate Research Centre
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. The student will have the opportunity to work with experts in the data analytics and condition monitoring field, as well as being part of our strong and dynamic research centre at Cranfield University
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excitation and ionization, photoionization time delays in multi-centre molecules driven by ultra-short XFEL pulses as well as to control the interplay of electron and nuclear dynamics during the break-up
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of multi-scale composites using graphene/nanoparticles reinforcement in glass/carbon epoxy composites to increase the delamination resistance. Graphene/nanoparticles, due to its nano dimension, can reinforce
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, the PhD student at ETRO will contribute to the core of the AI pipeline by designing and validating generative models (e.g. latent diffusion-, autoencoder-, and GAN-based approaches) that can infer
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datasets, modelling approaches, and performance metrics; develop physics-informed and data-efficient machine learning models to predict sorbent behaviour from sparse and multi-modal experimental data; and