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Are you passionate about advancing sustainable mobility solutions? Do you enjoy working at the intersection of artificial intelligence, optimization, and energy management? We invite applications
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on III-nitride devices and circuits for both high frequency and power applications. We will explore new concepts in III-nitride semiconductor material and device processing to optimize different important
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), enabling affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow
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), enabling affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow
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are looking for a curious and driven postdoctoral researcher to join a project focused on improving how we study and optimize medical treatments. The work centers on advancing a vessel-on-a-chip platform—a
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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systems. This PhD project, part of a national initiative, aims to use AI to design and optimize thermal interface materials (TIMs). It combines machine learning, materials informatics, and experiments
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. The postdoctoral project will be focused on the design of optimized generation schemes for tailor-made electron beam and radiation sources in laser-produced plasmas. As a postdoctoral scientist, you are employed by
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of numerical modelling (e.g. CFD, FEA, FSI, optimization, ML), but we are also involved in experiments and real-life monitoring to support our findings. Besides research, our division is actively involved in