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investigate and develop innovative memory solutions in advanced CMOS technologies such as FDSOI. The development of integrated circuits also plays an important role in making these networked devices and their
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simulations to explore which gene regulatory network architectures are necessary for robust regulation and effective communication between compartments. A third possibility is to simulate the evolutionary
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, multi-energy grids, and the integration of renewable energy and storage systems Your tasks In the framework of MSCA Doctoral Network (DN) Consumer Energy Demand Flexibility in Electricity Use (CoDEF
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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international research networks. The Division of Materials and Manufacture , part of the Department of Industrial and Materials Science, focuses on the full value chain—from materials design processing and
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
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to publications and other relevant communication Qualifications Relevant university master's degree Good theoretical background in organic chemistry, molecular biology or biotechnology Experience with analytical
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” highly flexible components and ensure their energy harvesting capabilities. This project will contribute to ambitious plans for SBSP as a vital part of the future Net Zero landscape. Recent advances in
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mentoring by a team of two interdisciplinary professors, RTG-specific scientific and personal development workshops, networking with academic and industrial leaders, the participation in national and
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? We invite applications for a PhD position, focusing on the design and implementation of Spiking Neural Networks (SNNs) using CMOS technology. Project Overview This PhD position is part of a