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computational biology/chemistry, machine-learning for biological or chemical data, metabolism, and drug discovery/design. Mentorship is taken seriously and every effort will be made to ensure the candidate is
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
Postdoctoral Positions: Computational Genomics · AI-Driven Precision Oncology · Translational Cancer Biology Wang Laboratory, UPMC Hillman Cancer Center Department of Pathology and Human Genetics
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and enthusiastic individual who meets the following criteria: Recently earned a Ph.D. in bioinformatics, computational biology, computer science, electrical and computer engineering, or a related
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. Applicants should hold a PhD in Computational Biology, AI/ML, Computer Science, Population Genetics, Bioinformatics, or a related field, and have fewer than five years of postdoctoral experience. Strong
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to therapeutic discovery You bring PhD in computational biology, bioinformatics, computer science, or related field Strong coding skills (Python required; ML frameworks preferred) First-author publication(s
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both computational and experimental biology to join our team in reimagining how we discover and deploy drug combinations in the clinic. Our work is highly interdisciplinary, integrating high-throughput
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a postdoctoral scholar in computational biology. The PI, Dr. Lixing Yang is an Associate Professor at the Ben May Department for Cancer Research and the Department of Human Genetics. To learn more
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influence the adaptability and evolvability of diploids and polyploids. Your profile You have a PhD in Computational Biology, Evolutionary Biology, (Bio)Engineering, Mathematics or Physics. You have expertise
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peptide, mRNA, or gene therapy development related to mitochondrial disease. Computational biology / bioinformatics, especially ribosome profiling, disease gene discovery, or integrative multi‑omic
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participating in the program will receive training in genomic analysis, experimental modeling, translational science, and preclinical modeling of childhood hematological malignancies. The training program will