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genomic, epigenomic, and fragmentomic data, from patient liquid biopsy samples Design and evaluate deep learning models for MRD detection and characterization Collaborate with multidisciplinary teams across
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& Responsibilities: Develop advanced deep learning methods for radiology or pathology medical imaging Integrate imaging data with EHR, clinical notes, or genomic data Conduct research on segmentation, classification
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to challenging research problems Outstanding theoretical background in machine learning and deep learning, demonstrated by excellent course grades and, where applicable, by research experience or scientific
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, providing them with a deep understanding of the field and hands-on experience in informatics research. In addition to the primary informatics and research responsibilities, there is the potential
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to advancing machine learning in biomedicine. The Program focuses on developing and applying cutting-edge AI approaches to address key challenges in molecular biology, clinical research, and translational
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editing and real time super-resolution imaging to the use of microfluidic devices and the development of deep learning segmentation tools. For their studies, the Conradt group uses the nematode
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for network modeling and regulatory module detection. Together, these groups aim to develop integrative, hybrid models that combine deep learning for genome interpretation with mechanistic biophysical