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Field
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biology and ecology, biomineralization, proteomics, bioinformatics, and/or molecular evolution. These skills will be applied in the design and execution of interdisciplinary experiments to investigate
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analyses of viral proteins using AI-based prediction tools will also be integrated to support evolutionary inference. This work lies at the intersection of evolutionary biology, bioinformatics, and data
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, stable isotope tracers, medical imaging). Experience with molecular biology techniques, stable isotope tracers methodologies, GCMS desirable. Familiarity with bioinformatics and computational biology
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Solid experience in nucleic acid isolation, PCR, RT-qPCR, and gel electrophoresis Solid experience in gene overexpression and/or silencing Proven knowledge in either four of these bioinformatics tools
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
appointment within the UNC Lineberger Comprehensive Cancer Center. Minimum Education and Experience Requirements PhD in Bioinformatics, Genetics, Genomics or equivalent or related field. Required Qualifications
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: Ph.D. in Computational Biology, Bioinformatics, Computer Science or related field. Demonstrated proficiency in Python or R, with experience building bioinformatic workflows and documenting code with
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cytometry will be an advantage. The project has a major computational component both for AI-driven modelling and predictions, and for bioinformatics analyses of wet-lab data. This will be performed by
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for a Postdoctoral Associate. The lab utilizes contemporary methods in molecular genetics, genomics, cell biology, biochemistry, bioinformatics and microbiology to answer basic and translational questions
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biology, biomedical research, bioinformatics, biochemistry, bioengineering, etc. With the motivation, skills, experiences, and initial achievements (subject to academic age) to qualify for a competitive
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in biology, forest sciences, environmental sciences, or data sciences. A strong background or motivation to learn ecological genetics and evolutionary biology as well as competence in bioinformatics