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This PhD programme is interdisciplinary and recruits PhD candidates with different academic backgrounds. Application and admission requirements About the programme The Study of Professions is a
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change accelerate, we urgently need smart, evidence-based tools to plan, manage, and protect our marine ecosystems. At the forefront of this innovation is machine learning. Its ability to process complex
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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It is widely accepted that synaptic plasticity constitutes the cellular correlate of learning. Yet, synaptic plasticity alone is insufficient to fully account for learning. Other mechanisms, such as
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. Empa is a research institution of the ETH Domain. In the Laboratory of Advanced Fibers, we are a multidisciplinary team of researchers working on different topics with particular focus on surface
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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ability to evaluate fossil fuel CO2 (ffCO2) emissions is currently limited. ‘Bottom-up’ emissions estimates, based on inventory-style accounting and mobile tracking data, can differ significantly from each
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results align with the abundance and expression of diatom ppRHO genes in the surface oceans. Research methodology Objective 1: Identify the subcellular localisation of two different ppRHOs isolated from
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ecosystem services such as carbon storage (1-4). Recent advances in satellite observations and machine learning provide novel opportunities to study extreme fires on a global scale. In a changing climate
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(e.g., in Python) and familiarity with different deep learning architectures. Good oral and written presentation skills in Norwegian Personal characteristics To complete a doctoral degree (PhD), it is