BACKGROUND

RESPONSIBILITIES

• Development of biologically-motivated algorithms which operate over complex biological networks and experimental data
• Pathway identification, characterization, and analysis approaches
• Drug target characterization and validation

REQUIREMENTS

• Interest in comparative genome-scale analysis of protein, gene, and metabolic networks, pathways, regulatory networks, and gene expression data analysis
• Basic knowledge of biochemistry and cell biology, including signal transduction, metabolism, and transcriptional regulation
• Demonstrated ability to develop, implement, and validate innovative algorithms for the quantitative analysis of genome-scale datasets in a systems biology context
• Ph.D. or equivalent in Computational Biology, Computer Science, Mathematics, Physics or other relevant discipline
• Understanding of graph theory, algorithms, and applications
• Strong algorithm development skills, including complexity analysis and performance optimization
• Ability to comprehensively assess algorithmic approaches to pathway- and systems-biology-based data analysis: identify and effectively communicate similarities and differences from design, algorithm, statistics, and end-user value standpoints
• Ability to implement and validate algorithms using Java, Perl, or Python
• Familiarity with ontologies and knowledge representation
• Excellent writing and communication skills, with a particular emphasis on the ability to communicate algorithmic concepts to scientists and to make effective presentations to an interdisciplinary, cross-functional group
• Organized, detail-oriented, and self-motivated
• Flexible and works well as part of an interdisciplinary research team

PREFERENCES

• Experience working on drug discovery and with scientists in the pharmaceutical industry is strongly preferred
• Knowledge of molecular and cell biology techniques used to credibly demonstrate protein functions and biomarkers used to infer functions
• Experience analyzing gene expression, sequence, and proteomic data
• Familiarity with AI, text mining, or lexical semantics
• Experience with sequence classification algorithms (such as Pfam)
• Ability to use HMMs and Bayesian statistics
• Familiarity with statistical clustering and classification algorithms (eg. K-means)
• Knowledge of human diseases: molecular biology, models, stages, and markers

CONTACT