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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 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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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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the project: Development of a regional normative BrainAGE model for morphometric measures. The position is part of the EU-funded Marie Skłodowska-Curie Actions Doctoral Network MenoBrain “Brain Health in
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, adversarial attacks, and Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering