51 parallel-processing-bioinformatics Postdoctoral research jobs at Yale University in Postdoctoral
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The Barak and Gunel Laboratories in the Department of Neurosurgery at Yale School of Medicine are seeking a postdoctoral fellow with a strong bioinformatics background to lead the computational
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-affiliated with the Yale Stem Cell Center, the Department of Comparative Medicine, and the Department of Surgery, providing a highly collaborative and multidisciplinary ecosystem. The position is ideal
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Yale Analog and RF Circuits and Systems (ARCS) Research Group, Department of Electrical and Computer Engineering, Yale University The Analog and RF Circuits and Systems Research Group in
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“drylab” techniques. Current drylab approaches focus on bioinformatics, including emerging artificial intelligence models. Current wetlab approaches focus on RNA and chromatin biology. Candidates should
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Congress Avenue, New Haven, CT Qualifications We’re looking for someone with a PhD, MD, or MD/PhD (or related field such as genetics, immunology, computational biology, systems biology, or bioinformatics
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spatial omics, longitudinal biomarkers, computer vision, electronic health data) with cutting-edge AI, we aim to fundamentally transform Parkinson’s disease from a disease without cures into a predictable
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, or computational analysis (e.g., R, Python, UNIX, bioinformatic pipelines) is highly desirable. Candidates should have a strong publication record, excellent communication and organizational skills, and the drive
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experimental approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory
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field. Strong record of creative and productive research (publications, presentations). Experience in molecular biology, RNA biochemistry, and bioinformatics is an asset. Interest in participating in
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approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory grammar