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data analysis (e.g., MRI preprocessing, statistical modeling, familiarity with tools such as FSL, SPM, AFNI, or similar platforms). Proficiency in statistical programming (e.g., R, Python, MATLAB
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the supervision of Dr. Joseph Kwon. We’re seeking a creative Postdoctoral Scholar to join our team and advance cutting-edge multiscale and hybrid modeling—integrating first-principles simulations with
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, immunology, molecular biology, proteomics, animal modeling and mouse genetics, translational medicine, and/or the use of human tumor models of cancers. Develops, adopts and implements new laboratory methods
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infrastructure in the form of a university-wide computing cluster; develop robust data/model commons integrated with cutting-edge computing clusters that will help democratize access to high-performance computing
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and non-myocyte cells; utilizes CRISPR-Cas9 genome editing to model genetic cardiovascular diseases; performs molecular biology techniques (qPCR, Western blotting, cloning, library preparation for omics
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in iPSC-derived cellular models. The position is ideal for someone hoping to be involved in grant-funded research as well as increase their publication record during postdoc and launch an independent
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expertise in metabolism and cardiac physiology with technical skills in metabolic activity measurements, mouse model handling, in vivo physiological measurements, PCR, Western blot analysis, experience
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AI/ML domain; build a scalable shared infrastructure in the form of a university-wide computing cluster; develop robust data/model commons integrated with cutting-edge computing clusters that will help
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. Ideally candidates will have expertise in both quantitative methodologies (e.g., structural equation modeling, multilevel regression, survey development validation, item-response theory, etc.) and
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. Candidates should have a strong research record in LLM-based agents, reinforcement learning, or large language models, preferably in areas closely aligned with the topics outlined above. Desired: We are