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is to predict and reproduce the architecture of these exoplanetary systems and the exoplanet properties, including composition, thereby testing currently competing planet formation models against
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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, as well as from industry. The successful candidate will work in the established collaboration between DSB and ICGI to develop multimodal deep learning models for predicting prostate cancer
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, is organised as a section at the Department of Geosciences. PHAB’s main goal is to develop predictive models to identify habitable planets around other stars. Within three different research themes: (1
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resilience of bridges under climate change-induced hazards such as flooding, scour, and debris impacts. The research aims to develop advanced numerical models and machine learning tools to predict loads
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descriptions, embeddings, metadata). Train and evaluate models for representation learning and prediction (e.g., PyTorch, Transformers, CNN backbones, contrastive/embedding objectives). Perform rigorous
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neurophysiological experiments - mathematical analysis of the dynamics of neural networks - programming and numerical simulations of neural networks - development of quantitative model predictions and
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functions to work properly. Please turn on JavaScript in your browser and try again. UiO/Anders Lien 1st March 2026 Languages English English English PhD Research Fellow in Mathematics and Fluid Mechanics
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, based on detailed studies of Earth and the solar system, is developing predictive models to identify habitable planets around other stars. Within three different research themes: (1) Planets and Early
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. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning models that forecast