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platform. Initially, a black box deep learning approach will be implemented. However, due to the need for robustness, transparency, and explainability (e.g. for quality control across sectors), the research
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for anomaly Detection and diagnostics: Leveraging state-of-the-art machine learning and deep learning models for automated fault detection, classification, and time-till-failure prediction. This will involve
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The Rasmussen Group focuses on development and application of computational algorithms such as machine and deep learning for analysis and integration of multi-omics and multi-modal data within cardiometabolic
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learning, deep learning, neural differential equations, diffusion models, flow matching, dynamical systems theory, control theory, model predictive control. You have excellent technical/computational skills
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project TARGET-AI will bring together expertise from multiple research groups to advance the state-of-the-art in combining the most advanced techniques from deep learning/AI with rigorous statistical
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of the PhD topic (subproject A7- Reinforcement learning for mode choice decisions): This PhD project will develop and implement a Deep Reinforcement Learning (DRL) model for dynamic mode choice within
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research with annual expenditures of more than $4.9 million in fiscal year 2024. Research activities are carried out in multiple research laboratories at the Department and at research centers in the College
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seeking a Research Assistant Professor that will be capable of contributing to multiple ongoing research projects in the lab. Potential projects include, but are not limited to, oceanographic
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tomorrow’s living environments? And how can we support mutual learning between researchers in the geo-information sciences and societal actors when engaging with an increasingly uncertain and turbulent future
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through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect on the performance of the CMF; Using machine-learning (deep learning) methods to develop a