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limited. We are offering a PhD scholarship for a student to develop ambitious new machine learning strategies for generating AI-ready data. You will work at the frontier of active learning and ML-guided
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. An external research stay of approximately 3 months in a collaborating institution is a mandatory part of this PhD fellowship position. You will work in close collaboration with experienced researchers and
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and industrial applications. In the long term, this work aims to contribute to a reduction in resource use and carbon emissions by improving the efficiency, reliability, and durability of concrete
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Margit Anne Petersen, map.crf@psy.au.dk Place of work Bartholins Allé 10, DK-8000 Aarhus C, Denmark Formal requirements You can read more about how to apply in the application guide and find the rules and
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experiments. Interest in connecting theory to experimental or real-world applications. Ability to work independently and communicate well in an international team. Research environment The PhD project will be
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. Develop and apply state-of-the-art electron microscopy methods to study molecules-adsorbents interfaces. Collaborate closely with TUM to correlate nanoscale insights with material performance. Contribute
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(28nm or below) Hardware-aware modeling of state-space inference pipelines Simulation and synthesis of the architecture using EDA tools Benchmarking against transformer-based hardware accelerators Work
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applications Co-integration of SSM-SNN cores with RISC-V processors in CMOS Hybrid analog/digital interfaces for spike generation and propagation Event-based communication protocols for ultra-low-power operation
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of research results in scientific journals and conferences. Qualifications: Good understanding of solid mechanics and preferably modeling of damage and/or fracture. Experience with experimental work and data
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is part of SDU’s strategic effort to advance PtX technologies through experimental validation and intelligent control. The research combines hands-on laboratory work with AI-based approaches