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Your Job: Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep
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of statistical analysis is required. Strong programming skills in Python, R and Linux-based software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing
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following table(s) for the current salary scale(s) for this position: https://www.ucop.edu/academic-personnel-programs/_files/2025-26/represented-oct-2025-scales/t23.pdf . The current minimum salary range
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, Artificial Intelligence, Computer Science, or an equivalent qualification. You have in-depth knowledge of deep learning techniques. You have experience with Python. You have in-depth knowledge of knowledge
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; Machine Learning• Clinical pathways and decision support for patients with acute chest pain• AutoPiX – Explainable Deep Learning for Multimodal and Longitudinal Imaging Biomarkers in Arthritis• Speaking
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Research Infrastructure? No Offer Description TASKS/ROLE * conducting research under the project Design-ready forward and inverse surrogate modeling of high-frequency structures using deep learning and
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, and research team to ensure timely achievement of project deliverables. Undertake the following specific responsibilities in the project: i. Develop, train, and optimise deep learning models for object
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, Statistics, Bioinformatics, Applied Math, or related field. MS or PhD degree is preferred but not required. Required qualifications ? Substantial expertise in training deep learning models and tuning large
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Particle Acceleration is looking for a PhD Student (f/m/d) Multimodal Reconstruction of Laser-Electron Accelerator Phase Space using Physics-Informed Deep Learning. Your tasks Understand the physical process
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qualifications PhD in Microbiology, Computational Biology, Genomics, Systems Biology, Statistics, Machine Learning or related discipline Publication record in Microbiology, Metagenomics, Metabolic/Statistical