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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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the structure and robustness of the ecological networks supporting reef fish communities at different positions along depth, latitude, and longitude gradients; challenging these networks under hypothesized future
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depth-related plasticity in the physiology and behaviour of mesophotic reef fish. Identifying depth-related changes in the structure and robustness of the ecological networks supporting reef fish
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the next. Your models will first be used to analyze completed experiments and identify trends, and later integrated into active learning and Bayesian optimization frameworks to suggest which experiments
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varying material properties. The resulting response will be analyzed using techniques such as Monte Carlo simulations. Identifying the variability of the model parameters using Bayesian inference
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insights that inform biodiversity management. The project includes: · Apply of deep learning models to annotate bird and bat species from sound recordings. · Develop a Bayesian statistical
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fully funded PhD position within the LowDataML doctoral network, focusing on developing innovative machine-learning approaches for drug discovery under low-data conditions. LowDataML aims to bridge
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-supervised learning, and few/zero-shot techniques — the student will adapt models to ecological data. Bayesian deep learning and ensemble methods will be explored for trustworthy uncertainty estimation
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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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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
(MSCA) doctoral network led by Prof. Cecilia Persson, Uppsala Universitet. Print4 Life – Advanced Research Training for Additive Manufacturing of the Biomaterials and Tissues of the Future https