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of the PhD student will touch upon various topics multi-body dynamics, optimal control theory, machine learning and robotics and artificial intelligence in general. The focus is broadly upon the development
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on the hypothesis that the future of building design lies at the intersection of physically sound building simulation models and machine learning (ML) techniques. Key considerations include effectively integrating ML
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-constrained models. Currently, we are advancing the development of single-cell models, machine learning approaches based on cultivation data, and the integration of metabolic models with computational fluid
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to the application deadline, with exceptions made for extended parental leave or military service*. Teaching experience: Demonstrable interest and potential to become a skilled educator are essential. A post-doctoral
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parental leave, sick leave or military service. The following experience will strengthen your application: Experimental atomic physics Optics Photonics Optomechanics Nanofabrication Nanomechanics Cryogenics
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theoretical physics Some computer programming knowledge, e.g., Python, C/C++, Julia, etc. Strong written and verbal communication skills in English Experience in the following areas is beneficial but not
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properties of superconducting circuits, both analytically and numerically. Familiarity with open quantum systems. Background in optimal control methods. Experience with machine learning for optimization
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/or reactor physics Documented knowledge/experience in machine learning What you will do As a PhD student, you will have the opportunity to shape your research project while receiving guidance and