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of the experimental approach will include: Bayesian reconstruction of events on billion-year timescales, determination of optimal embeddings and encodings for protein structures, multiple structural alignments
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novel metabolic vulnerabilities in KRAS-mutant lung cancer using in vivo and in vitro screening approaches; (ii) validate identified targets in vivo and elucidate the underlying mechanisms; and (iii
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Qualifications Minimum Education and Experience Ph.D. in Neuroscience, Cognitive Neuroscience, Neurobiology, or related field. Rigorous training and experience in statistics, data analysis, and human experimental
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required. Must have experience with and/or working knowledge of multiple experimental techniques, including one or more of the following techniques: PCR, RT-qPCR, ELISA, RNAseq, isolation of extracellular
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mechanisms of various brain disorders using murine models and iPSC-derived human neurons. The applicant should have strong background in neuroscience and/or biomedical engineering or computer science
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related science field required. Must have experience with and/or working knowledge of multiple experimental techniques, including one or more of the following techniques: PCR, RT-qPCR, ELISA, RNAseq
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of the cellular localization of B. thetaiotaomicron HTCS proteins. The research will involve both cellular and in vitro studies. In addition to experimental research and contributions to maintaining the laboratory
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to oversee research activities outlined in NSF Grant 2520154 “Understanding Expectation-Driven Learning in Early Childhood: An Experimental and Computational Investigation,” under the supervision of Dr. Kimele
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transformer architectures (e.g., ViT/TimeSformer, CLIP/BLIP or similar) in PyTorch, including scalable training on GPUs and reproducible experimentation. Demonstrated experience building explainable models (e.g
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researcher will be responsible for the design, analysis, interpretation, and presentation of experiments regarding the study of the disease mechanisms of various brain disorders using murine models and iPSC