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Field
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personalized psychiatry, network science, and recovery-oriented research; Interest in integrating neural, behavioral, and recovery-related outcomes; Excellent communication skills and the ability to work in
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implementations (e.g., biophysical models), as well as models of machine intelligence (e.g., deep convolutional neural networks). We test the models' predictions in our empirical studies with human participants
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patient-centred organizations across Europe. Through this collaborative, interdisciplinary network, our researchers will work at the frontier of personalized neuroscience. Where to apply Website https
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cells Key methods will include: Gaussian Processes (heteroscedastic & multivariate) Operator-valued and deep kernels Active Bayesian experimental design Physics-informed neural networks Closed-loop
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) modules into safety-critical embedded systems (autonomous vehicles, drones, industrial and medical devices) raises major safety and security concerns. These modules, often based on deep neural networks
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the design and analysis of such models. PhD position 1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD
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organizational levels of the brain – from molecular and cellular processes to complex neuronal networks and behavior. In association with the SFB 1436, Neural Resources of Cognition (supported by the German
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neuromorphic circuits. We will also simulate high-efficiency spiking neural networks (SNN) and build neuromorphic sensory systems to validate performance and explore broad biomedical and other potential
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, and the mathematical and computational foundations of neural networks. Familiarity with the following areas is meritorious: machine learning, computational complexity, tree automata and tree
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1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD position 2 will focus on designing scalable