Molecular simulation and modeling

• All-atom MD: setup, equilibration, production, stability validation
• Enhanced sampling & pathways: SMD / CF-SMD, NEB-style pathways, annealing hybrids
• Coarse-grained modeling: mapping strategies, CG design, CG pathway analysis
• Parameterization workflows: force-field aware validation, sensitivity & reproducibility checks

Energy and free-energy calculations

• Energy decomposition & interaction energies (protein–peptide, protein–surface, ion effects)
• Free-energy calculations & comparative energetics across states/conditions
• Energy-landscape interpretation: intermediates, barriers, mechanism-level conclusions

Docking, clustering, and structural analytics

• Protein–peptide docking workflows: pose generation, selection, refinement
• Clustering & convergence: hierarchical clustering, consensus binding trends
• Pocket/cavity-aware filtering: overlap scoring concepts for pose ensembles

Trajectory and biophysical analysis

• RMSD, RMSF, contacts, H-bonds, radius of gyration, clustering heatmaps
• Surface & hydrophobicity mapping: domain-specific interpretation, site overlays
• High-throughput automation: batch pipelines across replicas/domains/conditions

AI and machine learning for scientific insight

• ML-assisted pattern discovery (feature extraction, clustering, dimensionality reduction)
• Predictive/statistical modeling for trends, grouping, comparative outcomes
• Data-driven decision workflows: translating outputs into scientific action

Programming, data engineering, visualization

• Python scientific stack: scripting, automation, analysis tooling, figures
• SQL: structured querying for extraction, cleaning, downstream workflows
• Publication-grade visualization: journal-ready figures, heatmaps, dashboards

HPC and scaling

• GPU-accelerated simulation workflows & multi-GPU planning
• Parallel execution: replicas, scaling runs, compute-aware experiment design
• Performance troubleshooting: runtime, memory, and environment diagnostics

Scientific software development

• API + app development for research tools (e.g., FastAPI pipelines)
• Reproducible software: clean structure, version control, deployable tools
• Packaging/distribution mindset: secure, user-friendly apps from research code