Apply now Job no: 500701
Work type: Full-Time
Location: University City - Philadelphia, PA
Categories: Drexel University, College of Engineering

Job Summary

This research project aims to investigate the characteristics of layered biological feedback using computational methods. In natural biological systems, hierarchical layers of feedback regulation are prevalent, allowing healthy cells to react to changes in their environment and interact with other cells. Despite its widespread occurrence, biological feedback is not well understood due to significant differences from engineered feedback, such as the combination of processes with varying timescales and principles (e.g. transcription, translation, post-translation) and the utilization of biomolecules at low copy numbers. The research will examine how these uniquely biological principles work together to achieve feedback regulation. 

The selected candidate will create mechanistic models of gene regulatory networks with hierarchical layers of feedback regulation and investigate their emergent properties. To examine the emergent properties of layered biological feedback, the candidate will utilize mathematical and computational tools adapted from engineering, mathematics, and quantitative biology.

The goal of this project is to develop computational approaches for understanding biological feedback regulation. These computational approaches can advance the design of synthetic biological feedback to be engineered into regulatory/therapeutic cells to sense disease and restore health in spatially organized microbial communities present in chronic diseases.

Candidates will work closely with graduate students and other postdocs as well as other investigators at Drexel. Candidates will have access to state-of-the-art high-performance computing resources.

Essential Functions

• The candidate will develop, analyze, and program mechanistic models of gene regulatory circuits to explain emergent biological properties.
• The candidate will conduct analyses of biological data in conjunction with experimental collaborators.
• The candidate will use machine learning to develop surrogate models of gene regulatory circuits as needed.
• The candidate will prepare and deliver conference presentations, manuscripts for peer-reviewed publication and other reports as needed.
• The candidate will assist in mentoring and training of PhD and undergraduate students in the lab.
• The candidate will use Drexel’s high-performance computing resources in their analysis of models of gene regulatory circuits.
• Other duties as assigned.

Required Qualifications

• PhD or Doctorate in computational biology, biophysics, statistics, engineering, or a closely related field.
• 1-2 years of related experience, which could be concurrent with pursuit of PhD.
• Candidates holding a degree in biological / medical science are welcome to apply if they have a strong background in theoretical or computational research.
• Applicants must have experience of understanding and developing mathematical models of genetic circuits.
• Applicants must have advanced-level programming skills in Python, MATLAB, Mathematica, or another programming language.

Preferred Qualifications

• Candidates possessing expertise and/or experience with machine learning are also especially encouraged to apply.

Physical Demands

• Typically sitting at a desk/table

Location

• University City

Additional Information

This position is classified as exempt with a salary grade of I. For more information regarding Drexel’s Professional Staff salary structure, https://drexel.edu/hr/career/ducomp/salstructure/

Please review the Benefits Brochure for some information on our benefits offerings.

Special Instructions to the Applicant

Please make sure you upload your CV/resume and cover letter when submitting your application.

Review of applicants will begin once a suitable candidate pool is identified.

Advertised: Apr 3 2023 Eastern Daylight Time
Applications close: May 3 2023 Eastern Daylight Time

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