Computational Sciences Practitioner

NumPy, SciPy, pandas, and Jupyter workflows for research-grade analysis.

Surrogate models, property prediction, and active learning loops.

DFT, molecular dynamics, and ML potentials for materials screening.

GCM structure, downscaling, and emissions scenario interpretation.

Sequence alignment, variant calling, and differential expression analysis.

MPI, OpenMP, GPU offloading, and job scheduling on HPC clusters.

Matplotlib, Plotly, ParaView, and domain-specific plotting conventions.

Containerization, workflow managers (Snakemake, Nextflow), and DOIs.

Bayesian methods, hypothesis testing, and uncertainty quantification.

Embedding governing equations as constraints in ML models.

Diffusion models for molecule generation and protein structure sampling.

Do-calculus, instrumental variables, and counterfactual reasoning.

DOE, factorial designs, and sample-size planning for costly experiments.

EnKF, 4D-Var, and real-time integration of observations into models.

Literature review automation, manuscript drafting assistance, and citation tools.

Findable, accessible, interoperable, reusable data stewardship.