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construct these species-specific mechanistic network models based on both genomic comparisons and machine learning based species-specific module identification. You will integrate these network models
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achieved through a variety of optimization, machine learning or AI-based heuristics. Optimization of revenue stacking models for hydrogen assets that have to supply a number of market-based services
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of political violence: from the role and effectiveness of sanctions during military disputes to the increasing importance of non-state actors in war, and from the effect of non-governmental organizations
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description A wealth of academic research has recently tackled new dimensions and aspects of political violence: from the role and effectiveness of sanctions during military disputes to the increasing
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research area. Prior experience working with Neural Radiance Fields or Gaussian Splatting. Prospective applicants should have a strong academic record with a solid background in Machine Learning (Deep
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that can be used for training machine learning and deep learning models. You will work in tight collaboration with other researchers in Nijmegen, Delft and at the Hubrecht Institute (van Oudenaarden group
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, computational fluid mechanics, high-performance computing, and physics-informed machine learning. Affinity with physics-informed machine learning, computational VVUQ (verification, validation, and uncertainty
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strong academic record with a solid background in Machine Learning (Deep Learning, generative models, diffusion models). Knowledge in sensor data processing and radaris a plus. Good programming skills
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of biased decision-making in machine learning (ML)? This PhD position offers a unique opportunity to contribute to cutting-edge research in algorithmic fairness, ensuring that automated decision-making
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observations. Your major challenge is in model development, and there is room for you to develop machine learning applications in the field of firn modelling. If successful, your work will lay the foundation