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Field
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using machine learning and deep learning techniques to generate indicators that allow remote monitoring of restoration. Knowledge of remote sensing (e.g. GEDI, LiDAR, multispectral) and programming (e.g
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algorithms; experience in 3D/4D (X-ray tomography) image processing; experience in machine-/deep-learning based image analysis; knowledge of tomographic reconstruction methods; experience in materials research
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Electrophysiological signal processing of, e.g., EEG, ECG, EMG, etc. Health data science, incl. modern machine, and deep learning methods, Cloud-based platforms like MS Azure or Google Colab Health data standards, like
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foundations of deep learning, functional analysis and measure theory. You can find more information about our research area and our team on the website: http://www.olgamula.com. As a team, we understand the
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molecular dynamics and path integral simulation methods, machine learning techniques, and electronic structure techniques. Additional background in statistical mechanics and deep eutectic solvents is highly
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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imaging pipelines, and use deep learning to gain insight into biological processes. You will also gain direct exposure to cardiovascular physiology and rodent imaging in close collaboration with biologists
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this position you may also have the opportunity to teach courses offered in our department. These duties would be to prepare and deliver lectures, prepare homework assignments, quizzes and exams, hold office
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stimulating environment that engages the best and brightest faculty and students to conduct deep and impactful research. Our faculty's research expertise and strengths cover several key interdisciplinary areas
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industrial stakeholders, and we have ongoing collaborations with Fujifilm Diosynth, Opentrons, Lonza and Neochromsome. In collaboration with OccamBio Ltd, we aim at designing deep learning models to engineer