About the position:
A bioinformatics analyst or programmer position is immediately available at the lab of Dr. Zhandong Liu from the Baylor College of Medicine at the Neurological Research Institute (NRI). Primary responsibilities include application and development of bioinformatics analytics pipelines to multiple types of high-throughput genomics, transcriptomics, and proteomic data. The successful candidate will be expected to work closely with computational as well as wet-lab bench scientists.
Education and Required Experience:
- Minimum BS in Bioinformatics, Biostatistics, Computer Science, Statistics or related field. MS preferred.
- Minimum one year experience in mathematical and scientific programming language R or Matlab.
- Minimum one year experience in scripting language Python, Perl, or equivalent.
- Competence with UNIX environment.
- Strong communication and scientific reporting skills (verbal, written, and graphical).
Preferred Skills and Experience:
- Application of computational and statistical methods to large-scale molecular biology data.
- Familiarity with bioinformatics tools and biomedical sciences databases.
- Background in statistics and machine learning, but the candidate will be given opportunities to expand their training in these areas if desired.
- Strong data processing skills who enjoys working with numbers and solving complex problems.
- Excellent focus and management of tasks from multiple concurrent projects.
The position is open for candidate from all level and salary will be compensated according to qualifications.
The NRI brings together world experts in neuroscience, computer science, and applied mathematics to pursue collaborative, interdisciplinary basic and translational research on a variety of neurological and neurodevelopmental disorders. Website: http://www.nri.texaschildrens.org/
At the NRI, the laboratory of Dr. Zhandong Liu focuses on the integrative modeling of transcription and signaling pathway activation. The lab develops algorithms for the analysis of gene expression arrays and next-generation sequencing data and is especially interested in addressing fundamental biological questions through machine learning and mathematical modeling approaches. Website: http://www.liuzlab.org/