12 machine-learning-phd Fellowship positions at University of Texas Rio Grande Valley
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Challenge grant. The successful candidate will work closely with the Principal Investigators (PIs) to develop and implement innovative research integrating machine learning, computer vision, and wildlife
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on application expertise and interest; Generation and analysis of mass spectrometry based exposomics data; Analysis of multiomics data; Analysis of MRI-derived imaging data; Provides guidance and support to PhD
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. Supervision Received General direction from the assigned supervisor. Supervision Given Guidance and support of PhD students in their dissertation projects as needed. Required Education Doctoral degree in Human
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generate data utilizing advanced instrumentation/techniques, flow cytometry, confocal imaging, live animal IVIS, and CT-PET machines. Designs in vitro and in vivo (murine model) experiments. Record and
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, confocal imaging, live animal IVIS and CT-PET machines, X-ray radiation, XCELLigence, Seahorse, ultracentrifuge, Proteomics (Mass spectra), HPLC, etc Conducts background research, independently design
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ecology. Equipment Use of personal computer and proficiency in software and research statistical applications. Use of gas chromatography-mass spectrometry (GC-MS) and related research equipment. Working
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Experience None. Preferred Experience None. Equipment Use of personal computer and proficiency in software and research statistical applications. Working Conditions Needs to be able to successfully perform all
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Experience None. Preferred Experience None. Equipment Use of personal computer and proficiency in software and research statistical applications. Working Conditions Needs to be able to successfully perform all
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, morphodynamic, and landscape evolution models. Field experience in geomorphological and hydrological surveys, including the use echosounders and Acoustic Equipment Use of personal computer and proficiency in
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. • Experience mentoring undergraduate or graduate students. Equipment Use of personal computer and proficiency in software and research statistical applications. Working Conditions Needs to be able