Postdoctoral Researcher, Chemical Engineering and Materials Science

Job Description

Location: Stevens Institute of Technology, Hoboken, NJ
Principal Investigator: Prof.

Pin-Kuang Lai
Position Type: Full-time, 1-year appointment (renewable depending on funding and performance)
 

Project Overview:

This position is supported by an industry-funded research collaboration between Stevens Institute of Technology and Janssen Research & Development, LLC.

The project, “Next Generation Viscosity Prediction for Molecular Liability Reduction and Multi-Parameter Optimization Development,” focuses on building machine learning models and coarse-grained simulations to predict concentration-dependent viscosity behavior of therapeutic monoclonal antibodies (mAbs) based on empirical and in-silico features.

Key Responsibilities:


* Lead the development and benchmarking of machine learning models (regression/classification) to predict mAb viscosity at various concentrations.


* Perform molecular dynamics (MD) simulations to compute spatial charge maps (SCM), spatial aggregation propensity (SAP), solvent-accessible surface areas (SASA), and other structural descriptors.


* Develop and implement feature engineering pipelines for protein sequences and structures, including integration of Rosetta- and Schrödinger-derived features.


* Construct and validate coarse-grained (CG) molecular models with hydrodynamic calculations to predict viscosity curves across concentration ranges.


* Collaborate closely with Janssen’s Computational Structural Engineering (CSE) and Biophysics teams through biweekly meetings.


* Prepare detailed project reports, including a six-month interim report and a final report for deliverables.


* Assist with manuscript preparation and publication of co-authored research results.

Qualifications:

Required:


* Ph.D.

in Chemical Engineering, Bioengineering, Computational Biophysics, Structural Biology, or a related field.


* Experience with molecular simulations (e.g., GROMACS, AMBER, NAMD) and coarse-grained modeling of biomolecules.


* Strong programming skills in Python and proficiency with machine learning libraries such as scikit-learn, PyTorch, or TensorFlow.


* Familiarity with protein structure analysis, homology modeling, and sequence-structure feature generation.


* Excellent written and verbal communication skills and ability to work independently and collaboratively.

Preferred:


* Experience with GPU-accelerated simulations and access to high-performance computing clusters.


* Background in biopharmaceutical formulation development, particularly mAb viscosity prediction or protein-protein interactions.


* Demonstrated track record of peer-reviewed publications in relevant fields.

Application Instructions:

Interested candidates should send the following materials to Prof.

Pin-Kuang Lai at plai3@stevens.edu:


* Cover letter detailing research interests and alignment with the project


* Curriculum Vitae (CV)


* Names and c...