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the degradation processes of micro/nanorobots. Here, computational methods are playing a transformative role, especially in the realm of enzyme engineering. These advanced tools are paving the way for more
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process, identification of parameters, variational modeling, and generative machine learning methods; see https://imsc.uni-graz.at/mr-dynamo for further details. As part of this research effort, we invite
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’ to learn new methods and techniques Possibility to obtain complementary skills Competitive salary and funding for at least 3 years Spirit, culture and hospitality of Austria, in the heart of Europe About the
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at Wiener Neustadt. Your tasks Fabrication and analysis of biphasic constructs using techniques such as 3D printing and casting, and characterization of biomaterials through methods like histology and
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particular by employing advanced methods from the field of artificial intelligence (AI) and its subfield machine learning (ML). Where to apply E-mail career@lec.tugraz.at Requirements Research FieldEngineering
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environment where your skills and expertise can be applied to solving complex healthcare challenges. We enable researchers to develop cutting-edge technologies, computational models and analytical methods
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discipline with very good or good results In-depth knowledge of psychoanalytic theories or methods (e.g. depth hermeneutic or biographical methods), particularly in relation to education, training and culture
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in NK/AML interaction (KL) and 3D culture (UWK), the PhD candidate will spend 50% of the working time at each laboratory. Methods: Molecular biology, cell culture, flow cytometry, microscopy YOUR TASKS
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-driven solutions is essential. We are looking for curious minds eager to acquire skills in computational methods and intelligent learning systems. A willingness to engage with the intersection of learning
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and reduction under the respective conditions Find a suitable method for model calibration and validation Perform sensitivity analyses to understand the impact of different parameters Interact closely