Post Doc Associate

The final salary offer may be determined by several factors, including, but not limited to, the candidate's qualifications, experience, and expertise, and availability of department or grant funds to support the position. We also take into consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university's broader compensation structure. We are committed to offering competitive and flexible compensation packages to attract and retain top talent.

Rutgers offers a comprehensive benefits package to eligible employees, based on position, which includes:

For detailed information on benefits and eligibility, please visit: http://uhr.rutgers.edu/benefits/benefits-overview.

In this position, the post doctorial associate will use robotics driven by machine learning / artificial intelligence to explore the self-assembly of polymers and their interactions with proteins. The Gormley Lab has been developing these data driven tools to explore highly complex problems in macromolecular chemistry. It is our strong belief that machine learning, when complemented with an automated workflow, provides an ideal platform for designing advanced nanomaterials that are perhaps too complex to rationally design. This project is highly cross-disciplinary and requires expertise in data science, machine learning, and programming in Python. Additional experience in polymer synthesis/characterization, macromolecular characterization, enzymes, and basic assays are ideal. Willing to consider candidates who are computational only, as long as they see themselves applying machine learning tools to physical problems in macromolecular chemistry. Due to the multidisciplinary nature of the project, the candidate will be expected to collaborate heavily with others in the Gormley Lab as well as other labs at Rutgers and Princeton University. We are hoping to hire two post doctorial associates for a set of projects. The listed projects are fully supported by NIH and NSF grants.

PhD in biomedical engineering, chemical engineering, chemistry, computer science, or related fields. Willing to consider candidates who are computational only, as long as they see themselves applying machine learning tools to physical problems in macromolecular chemistry.

PhD level is required because creative model design and training is inherently part of the development process.

Familiarity with materials (synthesis, functionalization, and characterization), macromolecules, and proteins is preferred. Evidence of a strong publication track record and working well in teams.

Familiarity with nanomaterials (synthesis, functionalization, and characterization) and enzymes is preferred. Evidence of a strong publication track record and working well in teams.

If experimental, UV-Vis plate reader, dynamic light scattering ( DLS), chromatography, and other basic equipment. Familiarity with SAXS/ SANS is also ideal.

Expect to spend several hours per day in the lab, which involves standing for long periods.