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passionate about applying ML algorithms and developing AI applied research and innovation solutions using classic ML to novel transformer neural networks. We test and measure the real customer impact of each
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impact-based health early warning systems. The successful candidate will join the research team of Dr. Joan Ballester Claramunt (https://www.joanballester.eu/ ) at ISGlobal within the framework
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work is the Department of Biostatistics (OCBE), Domus Medica, Gaustad UiO campus, Oslo. Job description The position is connected to the project “Bayesian Enhanced Tensor Factorization Embedding
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on hormonal time series data collected at unprecedented time resolution in healthy humans and in patients, including studies in real life settings with a state-of-the-art wearable device (https
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on hormonal time series data collected at unprecedented time resolution in healthy humans and in patients, including studies in real life settings with a state-of-the-art wearable device (https
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programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision
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, 4) Model interpretability. Experience with other deep learning methods, such as Convolutional or Bayesian Neural Networks, Simulation-Based Inference (SBI), Normalizing Flows, or Diffusion Models, is
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, e.g., by nationality (British Citizen) or 5+ years UK residency etc. Eligibility criteria and further information on the process can be found on the UK Government security vetting website, see https
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Your Job: This research primarily seeks to incorporate advanced neuron models, such as those capturing dendritic computation and probabilistic Bayesian network behavior, into unconventional
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, including Tikhonov regularization [3], Bayesian approaches [4], and compressive sensing or sparse regularization methods [5]. However, with the emergence of Physics-Informed Neural Networks (PINNs), new