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to solve biomedical problems, or a PhD in biomedical sciences with a strong interest to apply AI and machine learning approaches. With our strong commitment to translating research findings to actionable
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principals to problem solve work. ● Ability to maintain detailed records of experiments and outcomes. ● Ability to quickly learn and master computer programs, databases, and scientific applications
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collaborative environment. Topics of interest include: Optimization for Machine Learning: Preconditioning methods for large-scale and stochastic optimization. Efficient deep learning using low rank and compressed
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initio computations, molecular dynamics simulations, and machine learning models. Collaborate with other researchers within the group and external partners. Present research findings at conferences and
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learn and master various computer programs. Strong record of peer-reviewed publications. A PhD, MD or equivalent with prior relevant training in Immunology, Biology, Physiology, Biochemistry or related
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resulting from T regulatory (Treg) cells by conducting genetic screens to overcome this suppression and to enhance CAR T cells for lymphoma. On the other hand, we also seek to apply the lessons learned from
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skilled postdoctoral researcher to lead multifaceted machine learning projects harnessing large-scale genomic, transcriptomic, proteomic, and imaging and clinical data. The researcher will have access
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our team. We are looking for postdoc candidates to develop and apply cutting-edge technologies in spatial transcriptomics, single-cell sequencing, machine learning, and functional genomics
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Affairs. The FY25 minimum is $73,800. A fully funded postdoctoral fellowship is available in the Ophthalmic Microsystems Laboratory at Stanford, which is led by Charles DeBoer, MD, PhD in the Department
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, mathematics, physics, or a related field. The ideal candidate should demonstrate a record of publications in the area. Strong knowledge in machine learning, statistics and programming skills (R, Python