48 algorithms-"Prof"-"Washington-University-in-St"-"Prof" Fellowship positions in United States
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, focusing on applications within the healthcare, education, and environment sectors. Designs generative AI techniques and algorithms for data integration and computational models, with objectives to amplify
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with applications to aerospace systems Designing, implementing, and testing control algorithms in simulation and hardware platforms Contributing to publications and reports; presenting research findings
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to remove PFAS. To accomplish these goals, the candidate will participate in the development of AI/ML algorithms for the prediction of chemical properties, infrared and mass spectra, and ionization cross
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learning, and data science, focusing on applications within the healthcare, education, and environment sectors. Designs generative AI techniques and algorithms for data integration and computational models
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 16 hours ago
software tools in order to develop biometric algorithms for recognition, spoof detection, fairness in AI, template security, and explainability, and related research areas, as well as paper and report
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Medicine and Bioinformatics. The specific objectives of the project are to (i) deploy network analysis methods to genomic data (50%), and (ii) develop such algorithms including community detection algorithms
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objectives of the project are to (i) deploy network analysis methods to genomic data (50%), and (ii) develop such algorithms including community detection algorithms (50%). PhD in computer science, engineering
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science, with a particular focus on neuroscience applications. Designs AI techniques and algorithms for multimodal data fusion (e.g., MRI, EEG, cognitive and behavioral data, blood biomarkers, and
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and
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-informed machine learning (PIML) models for the prediction of physical and chemical properties using data from experiments and computation constrained by physics requirements. § Implementing algorithms