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Large-language models (LLMs) have impressive capabilities, such as automatically generating code, writing poetry, or summarizing text; but can they be used to automate the design of mechanical
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range of possibilities. Indeed, the ability to generate tissue-like materials from living building blocks whose mechanical and chemical interactions can be designed is a highly desirable goal of many
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to design, owing to the large parameter space and multidimensional design targets. In this pilot project, we want to investigate how artificial intelligence could predict the properties of two-dimensional
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ways in which such physical learning is realized, and design new types of learning machines capable of solving complex engineering problems on their own. Some examples include neuromorphic computers
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carefully designed resonators. With these, we see on the one hand to push the known boundaries of mechanical metrology: exploring whether quantum limits of displacement detection can be evaded by smart
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structures for application in high-precision semiconductor metrology, guided by design principles from the field of optical metasurfaces. The tremendous success of the semiconductor industry is enabled by
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hand to perfom metrology on structures in wafers that themselves emit at very short wavelengths when illuminated by infrared light. How do you design optical metasurfaces that efficiently radiate UV
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the following questions: Suppose optical signals are encoded in space like an image, in wavelength and polarization. How do you then design metasurfaces to perform common image processing steps, nowadays done
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explore new architectures for creating functional and smart hybrid nanosystems. AMOLF performs leading research on the fundamental physics and design foundations of natural and man-made complex matter, with
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focused on the fundamental aspects of understanding and characterizing how chiral amplification effects can occur during crystallization, or on the design and development of systems that can undergo these