A postdoctoral position is immediately available in the laboratory of Dr. Marcin Cieslik at the University of Michigan for candidates with a Ph.D. in computational biology and/or bioinformatics or relevant experience. We are looking for a talented Fellow to work on several projects at the interface of cancer genetics, genomics, and precision oncology.
A fitting candidate will take ownership of individual and collaborative projects, working closely with cancer biologists, clinicians, and pathologist. We are committed to providing a stimulating and nurturing environment for candidates with all career goals.
Broadly, our research aims to understand the role of inherited and acquired genetic variation in driving aggressive cancer phenotypes and enabling cancer metastasis. Therefore, ideal candidates will have experience in genomics, cancer genetics, and cancer biology. Candidates are expected to have expertise in programming and high-throughput analysis of genomic and biological data, as the position involves integration of large-scale data sets, design and implementation of data processing pipelines, and analyses of next-generation sequencing data.
Research areas of interest include but are not limited to the analysis of genomic data from cancer patients, development of novel statistical methods and algorithms for the analysis of cancer genomes and transcriptomes, development of innovative and large-scale biological databases, integration of genomics, proteomics, and functional data.
We are part of the Michigan Center for Translational Pathology, a world-leading and multi-disciplinary group committed to advancing our understanding of genetic factors involved in the development of cancer, and developing methods for early detection, diagnosis, and treatment of cancer.
● Excellent communication and teamwork skills to take advantage of the highly collaborative environment; adaptability and willingness to contribute to the overall goals of the research. ● Experience in high-throughput sequencing data analysis and familiarity with widely used genomic databases and software. ● An extensive grounding in bioinformatics and genomics, including an understanding of genomic structure, transcriptional regulation, and epigenomics. ● Programming experience in the a *NIX environment, with a demonstrated ability to design and implement algorithms in working code. ● A strong quantitative mindset and robust understanding of statistics ● High proficiency with at least one interpreted programming language and ability to implement algorithms in compiled languages: C/C++, Java. ● Coursework and practical experience in applying statistical and/or data-mining approaches to complex and noisy biological data.