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non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory grammar. Responsibilities
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-throughput experimental approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning
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predict molecular subtypes and therapeutic responses. These tools power a precision oncology framework that enables data-driven treatment selection. In parallel, we generate patient-derived 3D
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contribute to analyses and processing of data. They will become familiar with high performance computing environments and parallel processing, as well as cloud computing infrastructure. They will
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organisms. Please note that we have a parallel search for computational neuroscientist faculty members at https://apply.interfolio.com/190740 . The successful candidates will be embedded in a richly
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date is earliest possible. PI: David A. Alagpulinsa, PhD Email/Website: [email protected] Introduction The Alagpulinsa laboratory (The AlagsLab) in the Yale Center for Molecular and Systems
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ideal for candidates seeking to advance their technical (wet and/or dry lab techniques) and independent research skills prior to graduate school (PhD, MD/PhD or MD). Training/Responsibilities: The
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present at conferences Collaborate with cardiac surgeons, imaging scientists, and computer-vision faculty. Qualifications PhD (or equivalent) in Computer Science, Applied Math, or related field
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skillset of the individual. Candidates with PhD, or MD/PhD degrees and experience in a variety of molecular techniques, animal experiments, and data analytics are encouraged to apply. The ideal candidate
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rooted in basic biology and have a strong translational perspective towards developing novel therapeutics for cancer patients. Principal Investigator Background/Research : Dr. Zaidi (MD/PhD) is a