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have extensive knowledge on processes governing cross-shore transport and can use experimental data to develop predictive models. Experiences within numerical modelling of coastal processes is considered
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-supervised by Dirkjan Schokker Where to apply Website https://www.academictransfer.com/en/jobs/358086/phd-ai-models-to-identify-disea… Requirements Specific Requirements You are an accurate, structured, and
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research in data-driven nutrition, health, and food science. With large-scale diet and health data, omics data, biomarkers, digital food and health services, we establish predictive models for evaluation
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biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run high-performance numerical experiments
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digital twins be used to provide on-line predictions as to the future expected evolution of these critical properties as the basis for safe reinforcement learning (RL) for on-line optimal control”. In
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on electrophysiological approaches (MEEG, iEEG) and signal processing, while in Maastricht, the partner team provides ultra-high-field imaging (7T and 9.4T fMRI) and AI-based modeling. The PhD student will be enrolled
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English English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade
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computational models generate hypotheses and, with the help of partner labs, validate them in controlled systems. The end goal is a mechanistic and clinically relevant map of how CIN shapes cancer behavior and
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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