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, advanced imaging techniques and numerical modelling. About the role A successful candidate will be working on the EPSRC funded project New perspectives in photocatalysis and near-surface chemistry: catalysis
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Max Planck Institute for Nuclear Physics, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 17 days ago
Job Code: 10-2025 Job Offer from July 21, 2025 The Max Planck Institute for Nuclear Physics is one of 84 research institutes of the Max Planck Society for the Advancement of Science e.V. (MPG
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
). Probabilistic and reliability-based analysis applied to underground structures. Advanced subsurface characterization techniques integrating geotechnical and geophysical data. Geohazard mapping and modeling
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validate polarizable force field models. Responsibilities will include both macroscopic property analysis (e.g., density, diffusion coefficients, ion conductivity, interfacial tension) and atomistic-level
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and model advanced systems based on condensation, sorption, or hybrid thermal processes using solar and alternative energy sources. Conduct laboratory investigations and field trials to assess
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Pneumatic Tires, Structure-Process-Properties Relationships. As part of it, we are currently looking for a postdoc on machine learning for road characterization. How will you contribute? Do you have proven
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compensation structure. We are committed to offering competitive and flexible compensation packages to attract and retain top talent. Benefits Rutgers offers a comprehensive benefits package to eligible
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he successful candidate is expected to have experience with advanced functional neuroimaging data analysis methods and/or computational cognitive modeling. Preferred Qualifications Previous experience
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independently and in interdisciplinary teams. T he successful candidate is expected to have experience with advanced functional neuroimaging data analysis methods and/or computational cognitive modeling
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metagenomics assembly” funded by the Research Council of Finland in the research group of University Lecturer Leena Salmela. We develop models, algorithms and data structures for high throughput sequencing data