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machine learning to model network behavior from real-world measurements (e.g., [7]). Although promising, these approaches still face three major limitations: (i) they often rely on idealized and extensive
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for collaboration. You will also have the opportunity to develop your own research project aligned to the interests of the MND group. This could include new machine learning models or exploring a particular aspect of
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including antenna and/or microwave engineering * programming skills and working knowledge of Matlab programming environment * knowledge of mathematical modelling, machine learning and artificial intelligence
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. Into the second year, the project moves toward methodology refinement and Machine Learning integration. The student will execute a more ambitious cycle with a complex alloy system and integrate machine learning
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including: Multi-scale Modeling / Computational Biologist / Bioinformatician – Research Scientist Artificial Intelligence (AI) / Machine Learning (ML) – Research Scientist Experimental Immunologist – Research
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular
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Institute on advanced machine learning projects that use historical behavioral data to predict outcomes and inform strategies to improve engagement and solicitation effectiveness. Additionally, the Lead Data
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. Research topics include: Development and validation of DORIS data processing and modeling Implementation of improved models for DORIS satellites and ground systems Cross-analysis of DORIS and other geodetic
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years and in the relevant areas of Machine Learning / Artificial Intelligence, Credit Risk Modeling and Operations Optimization Modeling; The candidate must have strong programming skills in Python, and
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at the interface of biological physics, agent-based simulations and machine learning to turn quantitative imaging data into a mechanistic, testable model of spindle positioning. In particular, we expect