Translational Research Trainee
We are seeking a talented and enthusiastic computationally oriented research trainee to work with an interdisciplinary team of computational biologists, bioinformaticians, clinical researchers and other scientists to develop algorithms and methodologies for clinical applications, specifically for Glioblastoma Multiforme (GBM).
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TRTP is a two-year training program (subject to annual review) with the goal of translating systems science developed at ISB to clinical research at PSJH.
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Each trainee will be co-mentored by a ISB faculty and a clinician from PSJH, and will work within a specified ISB lab.
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Every trainee will receive funding to support salaries and research-associated costs for the first two years, and are encouraged and expected to secure funding as their research progresses.
Program Focus Area for 2019
Participating faculty: Nitin Baliga (ISB) and Santosh Kesari (John Wayne Cancer Institute)
The ideal candidate will:
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Integrate and analyze public (e.g. The Cancer Genome Atlas and others) and newly generated molecular data into our GBM SYGNAL network framework with a focus on regulatory mechanisms at different scales: genomic variations, DNA methylation, TFs, miRNA and chromatin profiles.
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Leverage heterogeneous cancer genomics data sets to predict tumor drug and immunotherapy responses and to develop human cancer therapeutics. Special emphasis will be placed on identifying synergistic combinations of new and previously established therapies that are matched to patient’s disease characteristics — i.e., personalized medicine.
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Develop new computational strategies to build networks from single-cell transcriptome data — comprehensive, integrative, and mechanistic gene regulatory networks with predictive power to enable rational design of effective personalized therapies.
This position will provide an excellent opportunity to work and train as part of a highly motivated interdisciplinary team in the rapidly developing areas of cancer genomics, precision cancer care and neurosciences.
Responsibilities
The work involves participation in and contribution to these larger projects through multi-site collaborations, but also offers the freedom to pursue one’s own research interests. Responsibilities include but are not limited to:
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Integrate data across multiple public datasets and platforms to identify new targets, evaluate external opportunities, and influence clinical trial decisions.
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Develop new and apply already-published computational approaches from patient profiling data to make actionable predictions for selection of therapy.
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Collaborate with research colleagues to interrogate clinical phenotypes and develop biomarkers that enable molecular segmentation of key disease subpopulations.
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Expected to generate, analyze, present, and publish results.
Qualifications
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Successful candidates must have PhD in computational biology or a related field.
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Strong analytical, programming and quantitative skills are essential.
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Preference will be given to candidates who have experience in cancer systems biology, or neuroinformatics, including the design and implementation of algorithms applied to the analysis of biological data.
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Experience with common bioinformatics methods, tools, websites and data resources is important, in particular, high-throughput data analysis tools and techniques, statistical analysis, genome sequence analysis, and experience in retrieving, manipulating and managing data from public data repositories such as TCGA, ENCODE, NCBI and Ensembl.
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Experience in machine learning, statistical inference and artificial intelligence and application of those tools to biomedical datasets. The ability to work with a variety of different biological data types is highly desired, as is experience working with cancer genomics data, in particular glioblastoma.
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Proven ability to collaborate and problem-solve productively as a member of an interdisciplinary team.
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Excellent verbal and written communication skills. Fluent verbal and written English language skills are required.