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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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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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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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structures like nanosheet transistors that are relevant for semiconductor manufacturing and uses tomographic techniques in combination with inverse design and machine learning tools. The aim is to determine
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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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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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Working conditions At the start of the traineeship your trainee plan will be set out, in consultation with your AMOLF supervisor. More information? For further information about the position, please contact
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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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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