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research work will be to devise efficient algorithms for source separation in DAS measurements. Issues such as large data volumes that can exceed 1 To per day and per fiber, instrument noise, complex nature
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manner. Collaborative Innovation: Lead and participate in collaborative initiatives aimed at developing novel computational tools, algorithms, and models that address critical challenges in drug discovery
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neurophysiological biomarkers with long-term neural activity recording and artificial intelligence algorithms to for conditions such as pain, Parkinson’s disease, sleep disorders, Alzheimer’s disease (AD), and
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molecular targets critical for developing new therapies for rare diseases, based on genetic data and biological system simulations. -Computational Drug Repurposing: Developing novel algorithms and databases
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health and disease, and experience in the algorithms used to analyze these datasets. The appointee will ultimately create an independent research effort with dedicated extramural funding that complements
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experience with algorithms relevant to computational biology documented programming skills, e.g. in Python and R very good communication and organizational skills with the ability to work to timelines, both
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analyzed. The tensor model structure estimated by suitable optimization algorithms, such as that recently developed in [GOU20], will be considered as a starting point. • Exploiting data multimodality and
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(morphological patterns), based on the experts’ knowledge. Then, tools like Procrustes analysis, linear dimensionality reduction (PCA) and standard clustering algorithms are employed. A first objective of our
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holistic view of interconnected biological systems in health and disease. We develop clearing technologies for cellular-level imaging and deep learning algorithms (AI) to analyze large imaging and molecular
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research environment focusing on integrating multi-source data and developing novel algorithms to address the challenges posed by global environmental change. You will focus on integrating experiments, field