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Field
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The Department of Public Health at the University of Copenhagen offers 1 PhD fellowship in biostatistics with special focus on methods development and theory for causal inference. The anticipated
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learning by using Bayesian learning principles. Among other things, Bayesian learning gives AI systems the ability to quantitatively express a degree of belief about a prediction or statement. By bridging
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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more information for a given experimental budget. Efficient active learning depends on the careful co-design of experiments and inference algorithms. You will explore topics such as how to elicit
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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary
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statistical inference. More specifically the recruited PhD student will consider variational inference approaches for GEA and stochastic optimization to speed up the inference, with the objective of scaling up
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framework will be used with advanced causal inference methods – including inverse probability weighting to construct a valid comparison group. The analysis will use the potential outcomes approach to address
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 4 days ago
, 3], significant challenges related to running complex AI algorithms such as Deep Neural Network (DNN) inference on lightweight platforms with limited computational power and relying on potentially
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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behaviour using computational approaches such as Bayesian program synthesis and inverse reinforcement learning. Investigate the diversity of motor commands that could implement observed behaviours and explore