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The Learning Machines group seeks motivated PhD students to join our team working on learning in physical systems. What are learning machines? Imagine your favorite artificial intelligence machine
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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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machines? In the hypersmart matter group at AMOLF, we want to answer this question in the context of smart nanomechanical systems. Nanomechanical systems lose very little energy when they vibrate, a property
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dynamics of cells that remains invisible with other techniques. You will develop AI training and inference approaches using our unique multi-parameter data-sets and extensive Machine Learning expertise
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rigorous self-consistent physical models into the machine learning method to achieve those goals. The Photonic Forces group at AMOLF, led by prof. Ewold Verhagen, seeks a motivated and talented postdoc
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that are relevant for semiconductor manufacturing and uses tomographic techniques in combination with inverse design and machine learning tools. The aim is to determine the possibilities and limitations of the use
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concepts and tools from statistical physics, fluid dynamics, network theory, and machine learning. A central aim is to illuminate fungal information processing and its underlying mechanisms with biophysical