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Field
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for dynamical systems, causality, and ML for science more broadly. We publish at core ML venues and are also interested in collaborating on applications in biomedicine, climate, or physics. Find out more at
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holistic multi-hazard risk framework capturing cascading effects across systems and scales; (2) the creation of digital environments utilizing real-time data for dynamic risk evaluation; (3) the advancement
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Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven, experimentally grounded workflow for rapid microstructure-property optimization in steels. The PhD student will
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dynamic, highly international team Excellent equipment and a collegial working environment Individual support and promotion of your scientific career through learning state-of-the-art techniques and
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dynamics of these devices to perform complex learning tasks with extreme energy efficiency. Your Job: Develop and apply meta-optimization that can automatically search for the best algorithm-hardware pair
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your doctoral degree: A dynamic, interdisciplinary research environment at the forefront of materials informatics Comprehensive training courses and individual opportunities for personal and professional
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at demonstrating how teacher learning can be made robust in an event-based framework, that is, when both the teacher model and the learning rules are event- based. The combination of excitable teacher dynamics and
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research position in a dynamic and collaborative environment Extensive opportunities for personal development and training, e.g. through an extensive range of training courses; a structured program of
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Your Job: Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so
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that define protein structures, functions, dynamics and interactions Protein structure prediction and modelling, e.g. in Rosetta, MODELLER, AlphaFold, etc. Protein-peptide complex prediction or docking