Postdoctoral Associate

The Sedlazeck lab is a highly interactive research group known for their outstanding contributions to computational biology and genomics. We drive the development of novel methods for large scale and individual genome applications and are worldwide known for our approaches which pushes the field of genomics forward. To achieve this, we have access to multiple thousands to millions of long-read genomic data sets that are available to us in addition to phenotypic data that allows us to study mechanisms and impact of mutations with a strong focus on Structural Variations. Structural Variations are large genomic alterations that, despite being ~1% of the numbers of SNP per human genome, impact more base pairs altogether.

 
We seek a motivated and talented postdoc bioinformatician who wants to join our group to work on human long-read applications to explore Structural Variations, Single Nucleotide, and other changes in a large population cohort. Over the next year we will produce a significant collection of long-reads across individuals of the USA population with the goal to deepen our understanding of mutations ranging from small (SNPs) to large (Structural Variations) and the impact of these mutations. In addition, we want to further the development of key software (e.g. Sniffles) and annotation resources. This will help us to better understand the mechanisms that drive mutations and their implications to individuals as well as ethnicities. This is a unique opportunity to drive the analysis and development of methods in prestigious settings and push the field of long read sequencing forward.

 
The position provides great opportunities to drive and develop your own research while having the data and support of a highly established group. You will have access to a variety of data sets from a large spectrum of diseases, and time to develop your own research track to enable your success in the next stage of academics. We also have strong ties to a number of technology companies and journals around the world, allowing us to gain access to cutting-edge technologies and publish our findings in high-impact journals. This position requires a trained bioinformatician, preferably with experience in programming, genomics and sequencing analysis. We are looking for an enthusiastic, driven team player with excellent written and verbal communication skills. There will also be opportunities to contribute to other projects at the HGSC- Baylor College of medicine that drive novel discoveries in areas of human health and precision medicine across multiple complex diseases.
 

Job Duties

• Performs in-depth sequence analyses on large genomic datasets of long-read.
• Performs mapping / assembly, variant calling, interpretation/annotation.
• Applies existing/ develop novel bioinformatics software to both well-defined and novel genome analysis problems.
• Researches, assesses, and customizes third-party bioinformatics software for utilization in projects.
  

Minimum Qualifications

• MD or Ph.D. in Basic Science, Health Science, or a related field.
• No experience required.

Preferred Qualifications

• A Ph.D. degree in Computer Science, Applied Mathematics, or related fields with a dissertation related to Computational Biology.
• Strong programming (Python/Perl/C++) and analytical skills, including experience with next- or Third-generation sequencing data.
• Expertise in working with the Linux/Unix command line (bash, make, software management and versioning via GitHub)
• Proficiency in handling large-scale scientific data sets and cluster computing environments (SGE/Slurm).
• Ability to independently develop reports and manuscripts and present findings in tabular, oral, written, and graphic formats.
• Ability to contribute independently and collaboratively, take ownership of assigned tasks, and excel in a fast-paced environment
• Published at least one first author paper in English and demonstrated written communication skills in English.
  

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.

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