Translational Genomics Fellowship Opportunities
Investigators in the Laboratory of Translational Genomics (LTG) work with fellows to develop new approaches to the study of the genetic basis of cancer and its outcomes. LTG laboratories, directed by independent investigators, have expertise in and conduct studies utilizing:
- High-throughput, sequence-based approaches to assess gene expression, splicing, chromatin interactions, and gene regulatory effects
- Molecular and cell biology methods to evaluate the biological consequences of altered regulation or function of susceptibility genes
- Population genetics
- Interaction between genes and environmental factors
Learn more about the Laboratory of Translational Genomics research areas.
Apply to be a Fellow in LTG
Candidates must hold a doctoral degree in medicine, genetics, epidemiology, bioinformatics, molecular biology or a related field. Individuals with laboratory experience and/or expertise in manipulating and analyzing dense genomic data sets are encouraged to apply. Individuals with clinical or epidemiologic training are encouraged to apply.
Fellowship applications are accepted on a continuous basis. Contact investigators in LTG doing research in your area of interest with a copy of your CV; DCEG scientists are always on the lookout for new trainees. In addition, submit your CV to the application database so it can be reviewed by investigators across the NCI searching for trainees. You can also apply for training positions with specific investigators listed below.
Training Opportunities with Specific Investigators
To explore training opportunities in other research areas, see a full list of the DCEG research groups on Apply for Fellowships page.
My mentor Dr. Amundadottir is providing a training environment that is well suited to the needs for my professional development. She is approachable and always very helpful with advising me on the direction of the research. In addition, unique to DCEG are the fantastic collaborators who are happy to help with model simulation and functional work, and access to large-scale Genome Wide Association Study (GWAS) datasets which are a powerful resource to study cancer susceptibility.