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Field
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this, the Fellow will implement a universal design methodology for such fluids of complex rheology, using a Machine Learning (ML) algorithm to be incorporated in a Computational Fluid Dynamics framework. Training
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: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design Creating and validating digital twin architectures that incorporate physical laws and constraints
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) with hardware in the loop functionalities to test and validate AI-based solutions in complex operation environments Working with rapid prototyping tools (dSPACE) to recreate charging profiles and
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, reflected in demonstratable outputs. A proven ability to manage complex projects to completion on schedule. Excellent communication and collaboration skills, with experience or willingness in engaging diverse
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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network at ETH and UZH, have access to state-of-the-art facilities and a variety of stimulating internal and external seminars. Job description We are looking for an intrinsically motivated, creative person
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of support in career development, networking, administrative and technical support functions, along with good employment conditions. More information about the department is available at: https://www.umu.se/en
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and networking. Who we are looking for We are looking for a postdoctoral researcher with demonstrated skills and experience in: The fields of star-forming molecular clouds, multi-scale structure
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frameworks of star formation. Our research team is active in a number of international collaborations and the position includes ample possibilities for building collaborations and networking. Who we
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the advent of the Internet of Things (IoT), any sensor can be interfaced with a local network or the Internet. This massive deployment has created many security issues and associated solutions