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Field
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collaborate extensively with national and international consortia. Projects include genome-wide associations and whole exome/genome sequencing studies. The successful candidate will have the opportunity to work
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have experience and/or be comfortable in dealing with the at least one of the following high-dimensional data-types: GWAS, RNA-sequencing, proteomic profiling, behavioural, etc. For this project
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, such as the Alzheimer’s Disease Sequencing Project and UK Biobank, and serve as the Biomarker Core for the Stanford Alzheimer’s Disease Research Center, curating and analyzing a deeply-phenotyped dementia
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processes that use data-driven machine learning. Given the span of the IN-CYPHER programme, we are seeking multiple motivated research fellows. Unique in its scope, we are developing technologies that span
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processes that use data-driven machine learning. Given the span of the IN-CYPHER programme, we are seeking multiple motivated research fellows. Unique in its scope, we are developing technologies that span
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, epigenomic, proteomic, T-cell receptor rearrangement sequencing, high dimensional spectral flow cytometry, in vitro organoid, and in vivo xenotransplantation assays. The Parekh lab is funded by R01 grants from
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of planet formation (such as condensation sequence, stellar and protoplanetary disc’s elemental abundances for major rock-forming and volatile elements) Fluent oral and written communication skills in English
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including R, and Perl or Python Experience in AI/ML modeling Knowledge of genetics, next-generation sequencing, genome assembly and annotation Strong oral and written communication skills Evidence of research
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of molecular biology and next generation sequencing is highly preferred. 3. Familiarity with single cell data analysis is preferred, but not required. 4. Ability to work independently as well as part of
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clinical trial patient samples, and in vitro or in vivo assays (genomic sequencing, RNA sequencing) Use of spatial biology methods (e.g. Xenium, GEOMx, CyCIF, HALO AI) to examine tissue responses in models