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learning algorithms. Collaborating with industry partners to understand operational requirements and developing AI pipelines for analysing visual and sensor data collected during inspection processes
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University of California San Francisco | San Francisco, California | United States | about 2 months ago
interpretation algorithms. A primary application will be segmentation and quantitative analysis of PSMA PET imaging in prostate cancer, with additional projects spanning radiology-based AI applications across
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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closely connected with quantum technologies and physical platforms. Relevant areas include but are not limited to: quantum information and computation; quantum algorithms and complexity; quantum error
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learning, edge computing, development of novel AI algorithms for agricultural applications, embodied AI, simulation to reality transfer, and systems engineering incorporating big data and artificial
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/deep learning algorithms. Knowledge of systems biology approaches (e.g. genome-scale metabolic modeling. Proficient with R, Python or MATLAB. Desired Qualifications* Experience with machine learning
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. The Research Fellow/Senior Research Fellow will develop localization and sensing algorithms, implement them on real hardware platforms, design and conduct measurement campaigns, and build demonstrable prototypes
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develop novel learning-based control and policy optimization techniques. We're looking for a skilled machine learning (ML) engineer to develop cutting-edge AI algorithms for digital twin applications in
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the correctness, robustness and reliability of deep neural networks and AI-enabled software systems. Job Responsibilities: Develop novel methods and algorithms for the verification, testing and robustness analysis
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theory, such as method of moment (MoM). • Experienced in computational algorithm on Finite Element Method (FEM) • Possess knowledge of electromagnetic theory, such as Dyadic Green’s function and