Remote | Particle Physics & Nuclear Simulation Expert — $60–$90/hour

24-Mag•New York, NY

3d•Remote

About The Position

We are sharing a specialised part-time consulting opportunity for particle and nuclear physics experts with graduate-level expertise in computational physics, scientific programming, and research-grade simulation and analysis tools. This role supports the development of advanced scientific problem-solving tasks based on real computational physics workflows. Selected experts will design original graduate-level problems requiring simulation, numerical analysis, experimental reasoning, and specialised scientific software, then refine those problems through iterative testing.

Requirements

Master's degree, PhD, or equivalent research experience in Particle Physics, Nuclear Physics, High-Energy Physics, Computational Physics, or a closely related field
Strong hands-on experience with computational tools used in particle or nuclear physics
Demonstrated proficiency with scikit-hep or comparable specialised scientific software
Experience applying computational methods to real research or professional problems
Strong understanding of particle physics data analysis, cross-section calculations, or related numerical workflows
Strong Python programming skills
Comfortable working in Linux and terminal-based environments
Ability to design rigorous computational problems and independently verify their solutions

Nice To Haves

Knowledge of renormalisation-group methods or perturbative QCD is highly valued
Experience with Monte Carlo event generation or collider phenomenology is advantageous
Research publications, open-source contributions, or substantial professional scientific computing experience are highly valued
Experience with scientific teaching, problem-set design, or computational reproducibility is advantageous

Responsibilities

Computational Physics Problem Design: Create original graduate-level problems in particle and nuclear physics, develop tasks based on realistic research and computational workflows, design multi-step problems requiring genuine scientific reasoning rather than straightforward calculation, and construct reproducible tasks with clearly defined inputs, outputs, and validation criteria.
Particle Physics Analysis: Develop computational tasks involving particle physics data analysis, work with scikit-hep and related high-energy physics Python tools, design problems involving cross-section calculations and collider observables, and develop workflows requiring interpretation of simulated or experimental particle-physics data.
Nuclear & High-Energy Physics Modelling: Create tasks grounded in computational nuclear and high-energy physics, develop problems involving interaction models, numerical calculations, and simulation workflows, apply research-level understanding of theoretical and computational methods, and design scenarios requiring interpretation of partial numerical or simulated results.
QCD & Renormalisation Calculations: Develop problems involving perturbative QCD where relevant, work with renormalisation-group calculations and scale-dependent quantities, create tasks requiring careful interpretation of theoretical assumptions and numerical outputs, and evaluate whether computational approaches are physically and mathematically appropriate.
Monte Carlo & Collider Workflows: Design problems involving Monte Carlo event generation where relevant, apply experience with collider phenomenology and event-level analysis, create tasks requiring strategic simulation choices or parameter exploration, and interpret outputs from event-generation and analysis workflows.
Scientific Programming & Validation: Write computational problem setups, oracle functions, and solution validators, use Python to implement reproducible scientific workflows, verify numerical answers and expected outputs independently, identify solver limitations, numerical edge cases, and implementation failures, and document assumptions, parameters, dependencies, and expected results clearly.
Problem Testing & Refinement: Test computational tasks against advanced AI systems, analyse whether problems appropriately distinguish strong scientific reasoning from superficial pattern matching, identify tasks that are too easy, ambiguous, or computationally impractical, refine prompts, constraints, and expected outputs until the target difficulty is achieved, and ensure tasks remain scientifically accurate and objectively assessable.