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to develop methodologies for real-time modeling and inversion of geophysical well logs, with a particular focus on borehole electromagnetic data during drilling. This includes the further development
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strong skills in mathematical modeling and a clear motivation to work on applied geophysical problems. The ability to work independently and in a structured manner, along with strong collaboration skills
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domainspecific knowledge with data
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modeling is an advantage Knowledge of the northern North Sea rift system and the onshore geology of Norway is an advantage Applicants must be able to work independently and in a structured manner and
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dissecting the functionality of different EV subsets. Methods include, but are not limited to, in vitro culture of primary human cells and cell lines, organ-on-chip models, molecular biology, diverse
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/Artificial Intelligence Apply for this job See advertisement About the position Position as PhD Research Fellow in Machine Learning/Artificial Intelligence for Planetary Ground Penetrating Radar available
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PhD Research Fellowships: Artificial Intelligence Adoption, Sustainable Finance, and Twin Transition
on research, publishing and teaching tasks. Being structured and having a high work capacity. Demonstrating solid theoretical insight and interest. Language: Good proficiency in English, both oral and written
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part of a dynamic research environment focusing on the early mechanisms of neurodegeneration in Parkinson’s disease. The main tasks involve working with mouse models of Parkinson’s disease. Key
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modelling and simulation of three-dimensional turbulent reacting flows in a CFD code is a requirement. Applicants must be able to work independently and in a structured manner and demonstrate good
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. The appointment is for three years with research duties exclusively. Research topic The PhD Fellow will be affiliated with the following project "Fano manifolds, wall structures and torus fibrations" (Shape2030