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PhD Scholarship in Development of Cement-Free Living Building Materials for Sustainable Construction
microbially induced carbonate precipitation (MICP) technology (preferred) Knowledge of how microbes interact and affect and are affected by their physical-chemical environment (preferred) Experience in
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information Read more about our recruitment process here. The assessment of candidates for the position will be carried out by qualified experts. Shortlisting will be applied. This means that after
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contact persons at the bottom of the jobpost. Interviews are expected to be immediately after the assessment process. Further information Read more about our recruitment process here. The assessment
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to contact us. You will find contact persons at the bottom of the jobpost. Further information Read more about our recruitment process here. The assessment of candidates for the position will be carried out by
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multi-fidelity modelling, your research will advance and integrate three core elements: (i) physics-based multi-fidelity structural models enabling high-resolution analysis at fatigue-prone hot-spots
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to the abovementioned requirements. The Further Process After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the hiring committee. All
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-principles physical knowledge with data-driven learning to enable continuous, autonomous system oversight. Research objectives The project pursues two interconnected research directions: Continuous
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more about our recruitment process here. The assessment of candidates for the position will be carried out by qualified experts. Shortlisting will be applied. This means that after the application
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, Water Resources, Civil Engineering, Physics and Meteorology, Applied Mathematics, Computer Science/Engineering, or a comparable discipline. The MSc degree must be equivalent with the Danish MSc degree
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph