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Your Job: We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
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point-based PhorEau projections using a machine-learning model predicting tree species richness as a function of spatially explicit abiotic and biotic covariates, including satellite-derived data
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Areas: computer science; applied math; artificial intelligence; machine learning; statistics; engineering Appl Deadline: 2026/02/06 11:59PM (listed until 2026/06/19) Fellowship Description: Apply
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to PhD level in a technical subject, with relevant experience: Expertise in machine learning for audio, including speech, music and ambient sound generation Research publications in relevant areas, i.e
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at the rank of Research Assistant Professor in applied probability, data science, machine learning, and spatial statistics. Candidates with a strong background in the development of novel models and original
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Software Testing and Analysis Machine Learning and Large Language Models Web Systems and IoT Systems The candidate must possess strong programming skills in Java or Python and is willing to learn all
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and supervising in bachelor and master programs Where to apply Website https://www.academictransfer.com/en/jobs/357657/phd-developing-reconstructions-… Requirements Specific Requirements We are looking
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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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in the Nurse Anesthesiology Programs(see website: http://www.barry.edu/about/mission/ Teach anesthesia principles and basic sciences in didactic, simulation, and clinical settings and advise students
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application! We are looking for a PhD student in Statistics with placement at the Division of Statistics and Machine Learning, Department of Computer and Information Science. Your work assignments As a PhD