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barriers: a large input modality gap, as network data consists of diverse, non-textual formats like time-series metrics, graphs, and scalar values; the inefficiency and unreliability of answer generation
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machine learning for time series, geospatial data or dynamic models; ideally experience with deep learning frameworks (e.g., PyTorch). Strong analytical and conceptual skills for designing and interpreting
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We have the power of over 40,000 students and co-workers. Students who provide hope for the future. Co-workers who contribute to Linköping University meeting challenges of today. Our fundamental
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integrating multi-modal data (e.g., time series, geospatial data, text, and image - including both hydrological and human-related data) and capturing complex, non-linear relationships (Bommasani et al., 2021
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towards a new conceptual model that explains the individual dynamics of fatigue. The project will start with an analysis of already-collected data. You will use several forms of time series analysis
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 23 days ago
to financial and economic time series, working with programs such as MATLAB, Python, and C++. The chosen candidate will have the opportunity to take courses and attend seminars at MIT. The ideal candidate will
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European consortium, we bring together industry partners, municipalities, academia, learned societies, and patient organisations to cocreate, pilot, evaluate, and iterate a series of city-level strategies
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
for rapid and resilient decarbonization. Your research will combine time-series analysis, supervised and unsupervised learning, and explainable AI methods to uncover the dynamic patterns preceding
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
resilient decarbonization. Your research will combine time-series analysis, supervised and unsupervised learning, and explainable AI methods to uncover the dynamic patterns preceding technological
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bioremediation assays to identify new plasmid regulatory genes and determine how they manipulate bacteria. This knowledge will enable them to design, build and test a series of synthetic biodegradation plasmids