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Field
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algorithms Evaluate the machine learning models with observational data at site to regional scales Analyze and develop interpretation of results in collaboration with scientists and staff working
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-doctoral Associate will develop algorithms and theory for machine learning methods, as well as implement and apply ML methods to problems in domains such as computational biology and neuroscience. This is a
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to vertical flow immunoassays. (VFI). Experience in liquid nanoprinting of VFI membranes and design of experiments algorithms to simulate accelerated shelf-life testing is required. Applicants should be detail
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devices related to vertical flow immunoassays. (VFI). Experience in liquid nanoprinting of VFI membranes and design of experiments algorithms to simulate accelerated shelf-life testing is required
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impairment capturable with digital interfaces. Experience with agentic large language models. Experience in artificial intelligence to develop the algorithms for digital twins. Experience with both qualitative
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lab/field validation. - Strong DSP/algorithms and real-time control; proficiency in Python/Matlab. - Experience integrating optics with electronics and firmware. - Track record of publications in fiber
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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outcomes. The individual will be expected to develop stimulation strategies and testing algorithms, write code, and develop software. They will do extensive validation and testing, under the supervision
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of protocols for clinical translational studies. Programming and testing of software algorithms and routines for image reconstruction, image quality assessment, radiation dose estimation, image processing
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. Eventually, we aim to map these algorithms on to energy-efficient emerging devices. In addition, you may also explore applying LLMs to drive multimodal models in scientific domains towards deep reasoning. As a