Job Description:
The Newman Lab, in the Institute for Stem Cell Biology and Regenerative Medicine and the new Department of Biomedical Data Science at Stanford University, is seeking highly creative and driven postdoctoral fellows interested in working at the intersection of biomedical data science and cancer/stem cell biology. Successful applicants will be expected to leverage computational tools to address basic or clinical research questions in diverse areas of cancer/stem cell biology, including tumor differentiation and development, the cellular composition of the tumor microenvironment, and cell lineage relationships in malignant and normal tissues. Opportunities for wet lab biologists interested in data science will also be available. In addition, there will be ample opportunities to work closely with basic and clinical science collaborators, both at Stanford and elsewhere. The successful applicant will have completed (or be close to completing) a Ph.D. or M.D./Ph.D. in an applied quantitative discipline, such as computational biology, bioinformatics, or biostatistics, with a strong interest in either basic or translational research. A strong background in machine learning and predictive modeling is desired, as is previous experience in common programming languages (e.g., R, Python) and genomic data analysis. Candidates with training in related fields, or in a life sciences discipline without formal computational training, will be considered depending on fit. Prior evidence of ambition, productivity, and creativity are a must, and a track record of conference presentations and first author peer-reviewed publications will be expected. Applicants should enjoy thinking deeply and working independently but also enjoy collaborating in a dynamic, fast-paced team environment….
Additional Info:
A major goal of the lab is the development of innovative computational methods that advance our understanding of normal and neoplastic tissue composition at a molecular and cellular level (e.g., Nature Methods 2015, PMID 25822800). As part of this effort, we employ a variety of genomics approaches, including high throughput sequencing and emerging single cell profiling technologies. [Click Here to Access the Original Job Post]
The Newman Lab, in the Institute for Stem Cell Biology and Regenerative Medicine and the new Department of Biomedical Data Science at Stanford University, is seeking highly creative and driven postdoctoral fellows interested in working at the intersection of biomedical data science and cancer/stem cell biology. Successful applicants will be expected to leverage computational tools to address basic or clinical research questions in diverse areas of cancer/stem cell biology, including tumor differentiation and development, the cellular composition of the tumor microenvironment, and cell lineage relationships in malignant and normal tissues. Opportunities for wet lab biologists interested in data science will also be available. In addition, there will be ample opportunities to work closely with basic and clinical science collaborators, both at Stanford and elsewhere. The successful applicant will have completed (or be close to completing) a Ph.D. or M.D./Ph.D. in an applied quantitative discipline, such as computational biology, bioinformatics, or biostatistics, with a strong interest in either basic or translational research. A strong background in machine learning and predictive modeling is desired, as is previous experience in common programming languages (e.g., R, Python) and genomic data analysis. Candidates with training in related fields, or in a life sciences discipline without formal computational training, will be considered depending on fit. Prior evidence of ambition, productivity, and creativity are a must, and a track record of conference presentations and first author peer-reviewed publications will be expected. Applicants should enjoy thinking deeply and working independently but also enjoy collaborating in a dynamic, fast-paced team environment….
Requeriments :
Skills :
- Cancer biology
- Cell Biology
- genomic
- Machine Learning
- Modeling
- Programing in Python
- Programing Skills
- Programming in R
Areas :
Additional Info:
A major goal of the lab is the development of innovative computational methods that advance our understanding of normal and neoplastic tissue composition at a molecular and cellular level (e.g., Nature Methods 2015, PMID 25822800). As part of this effort, we employ a variety of genomics approaches, including high throughput sequencing and emerging single cell profiling technologies. [Click Here to Access the Original Job Post]