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research on novel brain machine interface technology. The role involves comprehensive testing of chronically implanted carbon fiber electrode arrays in neural recording and stimulation applications. Specific
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differential geometry, algebraic geometry, etc.) or for computer science (such as machine learning, linear logic, etc.). While the position start date is flexible, the successful applicant must have completed
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and processes; - Experience with machine learning and AI tools. Modes of Work Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work
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areas of statistics, data science, and artificial intelligence (AI). The position will focus on developing and applying novel statistical, machine-learning, and AI methods to advance biomedical and
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these activities. Other laboratory responsibilities, as they arise, under the direction of the PI. Required Qualifications* MD or PhD degree in biomedical sciences (or related field) with a minimum of 3 years
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dataset analysis, machine learning tools, and relevant computational biology approaches • Document, compile, and format data analysis in presentations and reports to supervisor. • Mentors and trains
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graphs (ARGs). Research areas include statistical/quantitative/population genetics, genealogical inference, machine learning, genetic prediction, genome-wide association studies, scalable linear mixed
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knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and
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interactions, and environmental stimuli, with applications in wearable technologies, intelligent sensing systems, human–machine interfaces, healthcare monitoring, and soft robotics. Key responsibilities include
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possible renewals, a cumulative period of four years of research fellowship intended for doctoral students. Preferential factors: Research experience in the areas of Bioinformatics and Machine Learning