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reconstruction (e.g., denoising and deep learning) and arterial spin labeling (ASL) imaging, as well as bio-statistician and clinical researchers, with an ultimate goal to overcome the existing technical
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The post-doctoral fellow will work with an interdisciplinary team of PIs and Co-Is within the Schulze Diabetes Institute. The projects focus on deep profiling of immune pathways that contribute
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the application of machine learning techniques (e.g., doc2vec, encoder models, multi-modal embeddings, large language models) to map concepts and their relationships, tracing how they change, merge, or diverge
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outcomes ●casual representation learning for real-world data ● deep learning interpretation, fairness and robustness ●Regularly conduct computational experiments to execute algorithms on various health and
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science environment. ● Proficiency in coding and advanced data analysis using languages such as R or Python. ● Experience working with databases using SQL, cleaning and transforming data. ● Deep
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for recording across multiple cortical/ striatal circuits •Optogenetic and electrical real-time perturbations, including interventions directly related to clinical deep brain stimulation. •Parametric