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. - Strong proficiency in machine learning, optimization algorithms, and computational modeling applied to construction systems. - Experience with designing and conducting experimental studies to evaluate
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Alexandria, Virginia. The focus of these positions will be on quantum computing, quantum algorithms, quantum learning, quantum error correction, and quantum fault-tolerance. The successful candidate will join
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Qualifications - Experience in developing algorithms for analysis of biological data. - Experience with single cell and spatial transcriptome data analysis. - Experience in supervised and unsupervised machine
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statement of interest and fit for the position, and a list of three references with contact information. Questions about the position can be directed to Dr. Lisa Gunter at lisagunter@vt.edu. Review
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on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual
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microenvironment. The project will employ genetically engineered mouse and human glioma models, along with advanced imaging techniques, single-cell and spatial transcriptomics, and molecular biology approaches
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, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military
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development. Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic
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. The successful candidate should also be detail oriented and enjoy a diverse working environment. Required Qualifications • PhD in biology, neuroscience, genetics or related field. PhD must be awarded no more than
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, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against