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data science, biomedical engineering, technical medicine, or a related field. You should have strong programming skills (Python, PyTorch), deep learning knowledge (multimodal learning, longitudinal
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 6 hours ago
cleaning, denoising, and prediction, including approaches based on statistical machine learning, deep learning, and Transformer architectures. Perform signal analysis in both the Fourier (frequency) domain
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at the forefront of medical science, and educators to advance learning. We are proud to be part of progress, working together with the communities we serve to share knowledge and bring greater understanding
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, Atmospheric Science, LSTMs, VAEs, PyTorch, TensorFlow, NumPy, pandas, scikit-learn, spatio-temporal datasets Additional Information Eligibility criteria Mandatory: *A PhD in Data Science, Artificial
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traditional custodians of the land, sea and waters of the areas upon which we live and work. We recognise their valuable contributions and deep connection to country and pay respect to Elders past and present
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for supervised and unsupervised learning. We devise deep learning models, which find application in imaging and image-based science, including in collaboration with domain scientists on and off campus in fields
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, Mathematics, Physics, or a related discipline Excellent programming skills in Python and/or R Strong expertise in machine learning, deep learning and predictive modelling Experience working with multimodal
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-resolution or single-molecule imaging. ● Experience with machine learning/deep learning for bioimage analysis, including model application, validation and/or development. ● Experience with optical development
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English PhD Research Fellow in Machine Learning and Statistics Apply for this job See advertisement About the position Integreat - the Norwegian Centre for Knowledge-driven Machine Learning
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the position of: Lecturer for the Module M06 'Advanced Deep Learning' 10 ECTS (18%) Start date: August 1st, 2027 Module completion: each autumn semester (beginning of August to end of January) Description