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learning, knowledge exchange, and joint knowledge creation. During these stays, 100% of the time of the PhD is dedicated to the research program. As part of NGinfra, the PhD candidate will also have ample
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technology for the forthcoming generation of highly energy-efficient computing systems. FeFETs are programmable, non-volatile silicon devices that enable innovative architectures to efficiently execute complex
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energetic constraints on circuit function. Deriving design principles for neural networks performing complex tasks. We employ computational and analytical methods from applied mathematics and physics
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cell types, neural circuits, and ultimately behavior. By uncovering the molecular and cellular basis of complex brain disorders, the program aims to pave the way for the development of novel, targeted
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network, PNAS 121, e2319718121 (2024) Qualifications We seek candidates with a strong background in physics, mechanical engineering, materials science, or computer science with an interest in complex meta
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-volatile silicon devices that enable innovative architectures to efficiently execute complex, irregular, and highly dynamic computational tasks. Realising computing systems based on technologies like FeFETs
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novel AI methods that make powerful models more transparent, robust, and usable in critical domains such as healthcare and smart industry. You will be part of a large national ‘Perspectief’ program
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’ program Foundation Models for Industry (FIND), collaborating with leading Dutch universities, knowledge institutes, and industry partners. The FIND project Foundation for Industry (FIND) – Large AI Models
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(UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 38.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The project focuses on the experimental realization and study of artificial