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, chemistry, and be willing to learn new disciplines and innovate to achieve the project goals. Additionally, ideal candidates would also have interests in areas such as: 3D printing, materials sciences
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using multimodal approaches including advanced imaging, nano-mechanical characterisation and machine learning techniques Developing physics-informed reliability models using experimental datasets
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CFD, thermofluids and machine learning. Experience in Python (or another language), machine learning frameworks, or CFD tools such as OpenFOAM is beneficial but not required. Applicants should hold (or
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cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with eight (8) years of experience; OR MS in the same fields with five (5) years
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birthplace of Albert Einstein (1879 to 1955), the world's most famous scientist. Are you interested in learning more? Check out this video featuring the highlights of Ulm . Activate map To activate the map
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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for application, but they may be required for university admission. Please mention in your application whether your prior education was in English. Knowledge of German is not necessary but we do encourage learning
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funding via an employment contract with the Max Planck Institute for the History of Science (MPIWG) or with Technische Universität Berlin and will have the possibility to teach. Two employment contracts
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to innovation and transition in action. Next to working in the context of the larger Ombion-CPBT project, as a PhD researcher you will have the opportunity to acquire and develop reflexive, participatory and
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expertise. In addition, programme participants acquire a certain breadth in the field as they interact with fellow students, participate in PhD training sessions and attend lecture series featuring prominent