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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 contact information for 2-3 references

• Links to relevant publications or software/code repositories (if available)

Department

Chemical Engineering and Material Science

EEO Statement: