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Field
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pioneering PhD project exploring how massive stars evolve into gravitational-wave sources. Combine stellar physics, simulations and cutting-edge data to uncover the origins of black holes and neutron stars
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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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. As part of this PhD, the candidate will: Conduct an integrative review of established competency models Create assessment tools (which may include use of AI tools) to measure CLMA proficiency
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, energy-related datasets. Proficiency in Python, MATLAB, and/or Julia for modeling, simulation, and data analysis. Familiarity with GIS tools (e.g. QGIS), time-series databases (e.g. InfluxDB), and version
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transitions, flow dynamics, stratification) inside thermal storages containing PCMs? How can we describe via validated multi-physics simulation models; heat transfer, flow behavior, and phase changes? Your task
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repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models
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powerful, modern analytical techniques including chemical proteomics and metabolomics. They will have access to advanced synthesis facilities, as well as biological models, such as macrophages and organoids
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will be trained in a variety of powerful, modern analytical techniques including chemical proteomics and metabolomics. They will have access to advanced synthesis facilities, as well as biological models
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powerful, modern analytical techniques and have access to advanced synthesis facilities, as well as biological models, such as macrophages and organoids, to enable the rapid translation of this research
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activities . You can also tailor your employment conditions through our Terms of Employment Options Model. In this way, we encourage you to keep investing in your personal and professional development