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Your Job: Machine Learning (ML) and artificial intelligence (AI) based on neural networks are currently reshaping all aspects of society. In several areas, such as medicine, AI-based tools
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offers a unique opportunity to work at the intersection of computational physics, neuroscience, and biomechanics, developing physics-based simulations that bridge neural circuits, muscle activity, and
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, to test the fault-ride-through ability of the converter. Expected Results A neural network structure trained on the limited dataset DT data with high-accuracy impedance estimation under different operating
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architectures (e.g., convolutional neural networks or transformers); deep specialization is not required. Exposure to Generative AI concepts and large language models (LLMs) is a plus. Proficiency in Python
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large-scale spiking neural networks. In close collaboration with our Mod4Comp partners (DFG Forschergruppe FOR 5880), you will develop models of performance and energy to guide the co-design of software
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Charité–Universitätsmedizin Berlin (Dr. Rosanna Sammons); for further information, see https://www.sfb1315.de/ - development of network models of the CA3 region of the hippocampus - investigation
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software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical
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candidate will work with open available datasets obtained in rodents and unique datasets of neural activity. Your primary focus will be to design new learning frameworks and neural network architectures
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to understand the basis for these affective symptoms. We focus on synaptic plasticity within genetically-defined neural circuits in the basal ganglia and thalamocortical networks. We combine patch clamp and in
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your predoctoral research, you will develop research skills, explore academic research careers, and network with a multidisciplinary community of scholars. The goal of the program is to enhance your