24 machine-learning-and-image-processing-"RMIT-University" Postdoctoral positions at University of Florida
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clinical features using machine learning and foundational modeling approaches. This work supports disease modeling across chronic kidney disease, acute kidney injury, cancer, and neurological conditions. A
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)-statistics, (applied) mathematics, or a related STEM field. Prior working experience with EHR data, machine learning, NLP, bioinformatics, and large language models (LLM) is preferred. In particular
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integrated circuits (IC) and printed circuit boards (PCB). Additionally, the candidate should demonstrate expertise in applying computer vision, image analysis techniques, machine learning, deep learning to IC
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transcriptomics, and 3D imaging. We are interested in the role of serotonin systems in the pathogenesis of Alzheimer’s disease and the intersection between AD and neuropsychiatric disorders. Highly enthusiastic and
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neurodegenerative diseases. ALS is the primary disease focus. The potential projects within this area include but are not limited to: Cell based assays (primarily imaging) for mitochondrial biology using human
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Qualifications: PhD in experimental particle physics at the time of appointment. Preferred: Deep understanding of the particle detectors, particle identification, data analysis Machine learning experience is a
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. The ideal candidate should have a strong background in one or more of the following: developmental biology, genetics, molecular techniques, bioinformatics/genomics, electrophysiology, calcium imaging
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paradigm of microbial NP discovery, providing unprecedented insights into the unexplored microbial taxa and ecological niches, and revealing the vast diversity of NP biosynthetic gene clusters (BGCs
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of soft materials, and mammalian and/or bacterial cell culture(s). Practical knowledge in high-throughput experimentation, confocal microscopy, and image analysis will be a plus. Must have PhD in Polymer
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, Environmental Science, Computer Science, or a closely related field Strong programming skills in Python or R Experience with machine learning and deep learning applied to geospatial data Demonstrated ability