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. Key qualifications include: Proven ability to develop and apply complex in vitro or ex vivo test systems. Hands-on experience with microbial assays, biofilm models, and/or biomaterial characterization
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northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering
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and economy that respect people and their environment. We are looking for our future postdoctoral researcher in model-based reinforcement learning to join the Computer Science and Networks (INFRES
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-informed AI framework that decodes the complex relationships between material defects, functional fields (e.g., strain, electrostatic potential), and device performance, with a primary focus on leveraging
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who has been working in an academic or commercial research laboratory/company related to the differentiation of human iPSC into different cell lineages, and the assembly of complex tissue culture models
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or (iv) develop new representations of phytoplankton physiology/productivity. The post will be based around using and/or developing the ocean model PISCES, which is a complex model representing the cycling
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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