Postdoctoral Appointee - Computational Biology/Systems Biology

Postdoctoral Appointee

Computational Biology/Systems Biology

Position Description:

The Mathematics and Computer Science Division at Argonne National Laboratory seeks a well-prepared postdoctoral appointee who will conduct basic research in computational biology and systems biology with the goal of understanding the protein and gene dynamics in microbial, plant, and microbiome systems. In this role, you will develop new algorithms to predict and simulate flux in microbial, plant, and microbiome systems, including modeling the dynamic response of these systems of genomic manipulation or environmental changes. Ultimately, the goal is to build a deep mechanistic understanding of how cellular systems manage limited resources to adapt to changes in the environment or their genome, including forces driving manipulation of protein levels, gene expression, and even evolutionary manipulation of the genome. 

You will also:

• Validate predictions by integrating proteomic, metabolomics, and transcriptomic data into models.
• Data and algorithms developed in projects will be integrated in the DOE systems biology knowledgebase (KBase).
• Participate in the conception and implementation of research programs in computational biology as part of an integrated, multi-institution research team.

Position Requirements:

• Comprehensive knowledge of computational biology with emphasis on metabolism, chemistry, and genomics.
• Comprehensive knowledge of linear algebra, linear programming, and optimization.
• Comprehensive knowledge of analysis methods applied to genome-scale metabolic models, including kinetic modeling and flux balance analysis
• Comprehensive experience and skills in interdisciplinary research involving computational biologists, computer scientists, and experimental biologists with in depth understanding of basic biological concepts primarily in microbial physiology and biochemistry
• Considerable knowledge of bioinformatics tools, algorithms, and data types.
• Considerable knowledge of mathematical theory, including statistics, sensitivity analysis, uncertainty analysis, sampling, and parameter fitting.
• Considerable knowledge of Python and Javascript programming languages.
• Considerable knowledge of data management and organization, including design of NoSQL databases.
• Considerable collaborative skills, including the ability to work well with scientists at other laboratories, universities, and in industry.
• Considerable independent judgment, the ability to abstract from specific problems to generate solutions.
• Good knowledge of metabolomics, proteomics, and transcriptomic dataGood understanding of kinetic reaction mechanisms.

This position is a TERM position. TERM Appointments are long-term temporary positions that can be renewed on an annual basis for up to three years.