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): ELEG (ELECTRICAL & COMPUTER ENGINEERING) Percentage of Time: 100% Union Code (Name): 99 (Non-Represented) Employee Class (Appointment Type): Staff (Career) FLSA Status: Exempt Classified Indicator
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personally and professionally. To perform maintenance on all medical x-ray equipment under the direction of the Physics and Engineering Division in order to produce the highest quality images consistent with
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imaging research experience with a background in applying advanced techniques and methodologies. Basic computer vision and image processing techniques with experience in real-world applications is preferred.
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department or the University under moderate supervision. Produces graphic designs and illustrations using computer and traditional methods. Formats and edits text. Prepares layouts and specifications for pre
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computer science, computer engineering, data science, or similar. Technical Skills or Knowledge: Experience with one or more machine learning and deep learning frameworks such as TensorFlow, PyTorch, or Keras
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developing and applying deep learning models, particularly in areas such as natural language processing (e.g. use of LLMs), computer vision (e.g. CNNs for image classification), and multimodal data integration
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of Parkinson’s disease and other synucleinopathies. The successful candidate will establish a collaborative research program on live-cell functional studies of alpha-synuclein aggregate processes in cell models
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accordance with SLA policies. Regularly image, configure, and install Windows and Macintosh systems in accordance with refresh and new hire computer policies. Perform system upgrades, patches, and preventative
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and lifestyle assessments. Analyze cross-sectional and longitudinal data linking microbiome profiles with behavioral, cognitive, imaging, and biomarker outcomes. Perform data processing, quality checks
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aspects of clinical animal magnetic resonance imaging (MRI), computed tomography (CT), Nuclear Medicine (NM) and positron emission tomography(PET/CT) imaging. Supporting animal and human imaging research