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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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Description We offer a deep immersion in bio-based energy technologies; the candidate will learn and live the translational perspective of designing biomaterials for sustainable energy-related applications
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
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advancements, population and economic trends, and their future developments come with deep uncertainties. Infrastructure policies must account for these uncertain drivers and their dynamic interaction with
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may also be appropriate for research projects requiring long-term strategies of building trust to gain access to the object of research. Fieldwork may consist of deep immersion in one place or research
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on your background, the work will 1) focus on the interaction between microwave design and measurement methods, looking deep into the technological capabilities of GaN, or 2) focus on new methods
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trends, and their future developments come with deep uncertainties. Infrastructure policies must account for these uncertain drivers and their dynamic interaction with changes in intended use. As part of
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Physics Operations Research Appl Deadline: (posted 2025/06/24, listed until 2026/06/23) Position Description: Apply Position Description Overview Susquehanna is expanding the Machine Learning group and