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Position Summary

This is a Postdoctoral Research Associate position. We are seeking a self-motivated individual with strong mathematical, physics and engineering background. The candidate is expected to have excellent computational skills, self-initiative, attention to details and ability to integrate experimental work from various projects into modeling. Background in Machine Learning (ML) and AI tools is desired. The person should also be able to plan and conduct numerical and physical experiments. Ideal candidate will collaborate with faculty, doctoral students, and postdocs within Center for Integrated Material Science and Engineering for Pharmaceutical Products (CIMSEPP) which is a NSF Industry-University Cooperative Research Center (IUCRC) at NJIT.

Essential Functions

• Develop a workflow for machine learning-accelerated characterization of microscopic surface roughness and shape morphology of crystalline, spray dried and/or amorphous solid dispersions.
• Integrate this information with property prediction tools coupled with experimentally measurable quantity into a coarse-grained mathematical model of adhesion for particle-scale discrete element simulation of particle flow in the testing and manufacturing equipment used in pharmaceutical research and industry. Example systems of interest include: (1) categorization of spray-dried dispersions (SDDs) to be able to add a ML layer for a hybrid particle property prediction system; (2) DEM simulations of simple geometries to study mixing and segregation of particle systems.

Additional Functions

Responsibilities include:

• Leading the development, calibration, and validation of computational workflows to quantify microscopic surface roughness from stereomicroscopic data
• Integrating experimental data, knowledge, and material parameters into the discrete element modeling (DEM) framework.
• Design and implement machine learning surrogates and coarse-graining strategies to accelerate DEM for industrially relevant pharmaceutical unit operations
• Supervision of undergraduate and Master's degree students on their related projects.
• Be the main point of contact (POC) for all mathematical modeling and computational methods.
• Disseminate results through peer-reviewed publications, conference presentations, and reports to the CIMSEPP Industrial Advisory Board (IAB).

Pre-requisite Qualifications

A Ph.D. in Engineering with significant mathematical modeling experience

Preferred Qualifications

A.Strong experience in modeling granular materials transport processes. Experience in discrete element model (DEM). Experience and interest in related experiments is a plus.

B.Excellent skills in programming and implementation of data analysis workflows in Python.

C.Excellent understanding of physics of particulate materials, especially for related contact and adhesion models used in DEM of pharmaceutical materials.

D.Good command of the English language, both written and oral. Multiple first-authored journal papers is a plus.

This position will be funded from the NSF CIMSEPP IAB grant and other sources. Initial offer is for one year and is renewable based on the availability of funding. Salary is at entry level.

• Develop a workflow for machine learning-accelerated characterization of microscopic surface roughness and shape morphology of crystalline, spray dried and/or amorphous solid dispersions.
• Integrate this information with property prediction tools coupled with experimentally measurable quantity into a coarse-grained mathematical model of adhesion for particle-scale discrete element simulation of particle flow in the testing and manufacturing equipment used in pharmaceutical research and industry. Example systems of interest include: (1) categorization of spray-dried dispersions (SDDs) to be able to add a ML layer for a hybrid particle property prediction system; (2) DEM simulations of simple geometries to study mixing and segregation of particle systems.

A Ph.D. in Engineering with significant mathematical modeling experience

At the university's discretion, the education and experience prerequisites may be exempted where the candidate can demonstrate to the satisfaction of the university an equivalent combination of education and experience specifically preparing the candidate for success in the position.

NJIT considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/training, key skills, internal peer equity, as well as, market and organizational considerations when extending an offer. This pay range represents base pay only and excludes any additional items such as incentives, bonuses, or other items.

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