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. Contribute to grant writing. Minimum Qualifications PhD in computer science, computational biology, or related field. Dissertation must have focused on development of neural networks for analysis of proteins
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analysis (e.g., seasonality). A fundamental understanding of Deep Neural Networks as applied to high-frequency time series datasets, including: Designing and implementing custom NN models in PyTorch
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parity-check codes and iterative decoders, investigating applications of quantum error correction in quantum computers and networks. We are interested in candidates with strong expertise in burst error
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, contributing to the development of innovative spatio-temporal analysis and engineering challenges associated with uncertainty. The role supports collaborative investigations, model development, and experimental
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include securing IRB approval, conducting Q-set analysis, and creating outreach materials. The associate will mentor research assistants, ensuring high-quality work and collaboration across the project
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the University of Arizona and relocations services, please click here . Duties & Responsibilities Core Research Activities Data Analysis and Interpretation: Analyzing data, drawing conclusions, and preparing
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of ecosystems. The project aims to: Improve our understanding of what controls net primary production of ecosystems. Develop upscaling methods to estimate whole-ecosystem transpiration from local measurements
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transpiration of ecosystems. The project aims to: Improve our understanding of what controls net primary production of ecosystems. Develop upscaling methods to estimate whole-ecosystem transpiration from local
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assigned. Knowledge, Skills & Abilities: Knowledge of coordinating experiments, and completing analysis, statistics, and interpretation. A strong team player. Ability to communicate in a clear, concise
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collaborations within the Department and with other units across the University. Derive, analyze, and format data for publications, presentations, and grant proposals. Perform statistical and graphical analysis