SEARCH CAREER OPPORTUNITIES

We are seeking a highly motivated individual with solid expertise in computational and statistical methods to analyze large datasets. The applicant will lead evaluation of single cell transcriptomic and chromatin accessibility data in the context of Type 1 diabetes relevant research questions. The individual will also closely interact with experimental biologists to design and evaluate their studies.

Depending on level of expertise of the individual, the position allows for developing computational and statistical methods in the context of ongoing projects. The position is a critical component in the characterization of Type 1 diabetes-relevant stem cell differentiation models and the molecular basis of the autoimmune response.

GENERAL SUMMARY OF POSITION:

Under the direction of a member of the faculty, the Bioinformatician is responsible for the design, development, evaluation and iterative modification of a technical infrastructure to expedite the quantitative evaluation of data resulting from studies that are laboratory based.  The position will entail the establishment and maintenance of applicable in-house bioinformatics resources and interaction with individual lab members on customized research projects, as well independent projects that are solely computational. 

The position responsibilities include

Perform computational analyses for a wide range of research projects.
Implement and adapt pipelines for high-throughput sequencing data analysis including single cell -omics and CRISPR screens.
Implement and adapt pipelines for cis regulatory element characterization, and DNA-binding protein motif searches.
Independently develop, implement and maintain custom designed computational solutions relevant for ongoing lab-based projects.
Implement and adapt machine learning methodologies for analysis and prediction of (epi-)genome function.

MAJOR RESPONSIBILITIES:

• Establish general bioinformatics resources for day-to-day use by members of the laboratory.
• In collaboration with a member of the faculty, generate customized programming solutions to improve user interaction with available bioinformatics resources
• Implement and adapt programs for microarray analysis, high-throughput sequencing data analysis, cis regulatory motif identification, and multi-genome protein motif searches.
• Independently develop, implement and maintain custom designed computational solutions relevant for ongoing lab-based projects.
• Independently develop, implement and maintain computational methods for meta-analysis of data generated in the lab as well as publicly available data.
• Independently execute a scientific computational project (e.g. a project that can result in a first author publication)
• Local establishment and customization of model organism genomic databases and tools for batch sequence analysis utilizing these resources.
• Interpret and present study results in support of laboratory members.
• Provide tabular and written summaries of approaches and analyses in a form suitable for inclusion in manuscripts or grant applications, as well as media for presentation at scientific meetings
• Develop and implement custom bioinformatics programming solutions in collaboration with lab as necessary
• Coordinate and collaborate with other research computing expertise at the Medical School as necessary
• Participate in conference calls and data management meetings as needed
• Perform other duties as required

REQUIRED QUALIFICATIONS:

• BS in computer science, or equivalent experience
• 1-3 years of related experience
• Co-authorships on peer-reviewed publications are strongly suggested but not required
• Strong background in statistical methodology, software languages and computer systems (Perl, C++, R, MySQL, etc.)
• Experience in writing basic search algorithms and the ability to generate new algorithms and programs for custom data manipulation and analysis
• Excellent communication skills, both oral and written, and interpersonal skills necessary to interact with a wide range of individuals.

PREFERRED QUALIFICATIONS:

• Experience with Next Generation Sequencing data (e.g. RNAseq, ChIPseq, ATAC-seq) 
• Experience in writing and implementing scripts (Python, R, shell)
• Experience using a computing cluster (LSF, SGE) 
• Background in machine learning (e.g. neural networks or SVM)