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regressions, Bayesian analyses). You preferably have experience supervising and/or teaching students. You preferably have knowledge of swarm robotics and/or deep learning artificial neural networks (e.g. CNN
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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theoretical understanding of statistical machine learning methods relevant to the project: Bayesian learning, machine learning, spiking neural networks. Experience of programming (e.g. with Python) and data
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focused on understanding and countering harmful narratives and, mis/disinformation, and applying social network analysis. To be successful you will need: PhD in a relevant discipline such as computer
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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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fresh perspective on how specialized brain networks can identify and categorize causes of sensory inputs, integrate information with other networks, and adapt to new stimuli. It proposes that perception
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of Denmark. The position is part of a larger EU project entitled “FEDORA - Federation of network optimisation services, simulation foresights, and data alchemy for adaptable, agile, secure, and resilient
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linear ballistic accumulator models, diffusion models, biased competition models, or Bayesian models. During the employment, the candidate is expected to engage in the development of computational models
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals