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Field
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: Artificial Intelligence, including but not limited to Machine Learning, Responsible AI, and Computer Vision; Theoretical Computer Science, including Algorithms, Algorithmic Fairness, Game Theory, and Learning
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networks, transformers, and diffusion models. Proven experience building AI solutions using classical ML algorithms such as decision trees, gradient boosting machines, and shallow neural networks
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supervision of Dr. Marwa Chafii on Channel Modeling for integrated sensing and communication (ISAC). Responsibilities: Lead the design, setup, and execution of ISAC channel measurement campaigns across multiple
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to synthesize sequencing-based analysis results e.g.,Rshiny, d3, plotly, ggplot2. A solid understanding of statistics and experience in the implementation of machine learning and statistical inference algorithms
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limitations or potential issues with datasets and data types that may impact performance from machine learning algorithms. Re-trains models as needed to address new challenges or novel goals. Aids investigators
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stakeholders, both internal and external. This will involve managing multiple communications channels and planning, creating and distributing high quality content to promote the Hub’s activities and impact. This
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issues or areas of critical data examination. Works with statistics on defining and documenting programming endpoint algorithms across a study, drug program and/or contributing to TA level algorithms
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of developing algorithms that are both technically robust and clinically relevant, ensuring that these innovations can be integrated seamlessly into existing imaging systems and workflows. Collaborating with
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for integrated sensing and communication (ISAC). Responsibilities: Lead the design, setup, and execution of ISAC channel measurement campaigns across multiple environments, hardware platforms, and frequency bands
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applications across a wide range of imaging and video processing fields beyond medical imaging. The Research Associate will be at the forefront of developing algorithms that are both technically robust and