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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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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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expertise is required in categorical data analysis, longitudinal data analysis, and risk modeling using statistical and machine learning approaches. Experience with supervising and managing clinical research
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. Subject matter expertise for area(s) of responsibility. PHYSICAL REQUIREMENTS*: Constantly perform desk-based computer tasks. Frequently sitting. Occasionally stand/walk, reach/work above shoulders, use a
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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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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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and to large, longer-term petabyte-scale storage (~6 PB). The computer cluster offers over 400 software modules (e.g., Gromacs, Gaussian, Mathematica, MATLAB, MOLPRO, Turbomole). Responsibilities
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selected Master and PhD students with financial support. Where to apply Website https://www.timeshighereducation.com/unijobs/listing/408577/cbs-assistant-profe… Requirements Additional Information STATUS
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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