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Qualifications: Master's degree in Agricultural related field and at least 4 years related experience in program development, delivery, and management. A relevant PhD may substitute for two years’ experience
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or industry equivalent work at a computing facility, or using/managing HPC resources Experience working with large scale machine learning models Experience with performance optimization, debugging, and
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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cases. We are particularly interested in how AI, Data Science, or Machine Learning techniques can be used to quantify and assess software and system security from open source software to cloud services
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project The main objective of this PhD project is to explore and analyze bio-inspired neural architectures for early detection from spatio-temporal data under realistic sensing and computational constraints
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networking and computer security, and genuine interest in the PhD project. We value a collaborative attitude and an interest in working both in teams and independently. Self-motivation, attention to detail
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for their stakeholders and society at large through our MBA, MS, PhD, and Executive Education programs. We are equally committed to cultivating new scholars and teachers and to creating and disseminating pathbreaking
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Your Job: In this position, you will be an active member of the SDL “Fluids & Solids Engineering” and will collaborate strongly with the SDL “Applied Machine Learning”. You will have the following
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projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome