Applied Physicist — Defence Modelling Lead (Hybrid)

Addition

Bath

Hybrid

GBP 54,000 - 90,000

Full time

2 days ago
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Benefits offered by this job

Hybrid working
Birthday off
26 days annual leave
High-spec machine
Team socials
Autonomy

Job summary

Addition Solutions is seeking an Applied Physicist to lead technically complex modelling across UK Defence programmes. You will build models for operational scenarios, platform performance, logistics and decision-making, turning stakeholder requirements into robust analytical approaches and technical specifications.

The ideal candidate holds a quantitative degree and has advanced modelling skills, with Python experience (NumPy/SciPy) and exposure to Monte Carlo simulation or Bayesian methods.

Qualifications

  • Degree-level qualification in highly quantitative discipline.
  • Experience delivering technical work in defence/aerospace environment.
  • Strong mathematical modelling and simulation skills.

Responsibilities

  • Lead mathematically complex modelling workstreams across UK Defence programmes.
  • Develop algorithms for signal processing, sensor fusion, and autonomous systems.
  • Collaborate with engineers and analysts to integrate models into simulation environments.
  • Produce high-quality technical reports and documentation.

Skills

Monte Carlo simulation
Bayesian inference
Numerical methods
Optimization
Statistical analysis
Python (NumPy/SciPy)
FORTRAN
Julia
Operational Analysis
MoD assessment frameworks

Education

Degree in Mathematics / Theoretical Physics / Applied Physics

Tools

Python (NumPy/SciPy)
FORTRAN
Julia

Job description

Addition Solutions is seeking an Applied Physicist to lead technically complex modelling across UK Defence programmes. You will build models for operational scenarios, platform performance, logistics and decision-making, turning stakeholder requirements into robust analytical approaches and technical specifications.

The ideal candidate holds a quantitative degree and has advanced modelling skills, with Python experience (NumPy/SciPy) and exposure to Monte Carlo simulation or Bayesian methods.

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