Research Scientist

Prana Tree is a fast-growing IT consulting and product innovation firm and wespecialize in AI, Quantum Computing, Blockchain, and Cloud solutions.

We are building new optimisation algorithms inspired by physics and field-theoretic ideas, designed to work with Ising machines, quantum annealers, and classical solvers.

This role focuses on algorithm design and experimentation, not just using existing tools. You'll work on QUBO/Ising formulations, post-processing and refinement layers, and the interaction between algorithms and annealing-style hardware.

What you'll work on

Designing and implementing physics-inspired optimization algorithms

Post-processing and refinement methods for Ising machines and annealers

Experimenting with energy landscapes, uncertainty, population statistics, and annealing dynamics

Benchmarking across classical simulators and specialized hardware

Writing research-quality code (Python and/or C++)

Background we're looking for

You might come from:

Applied mathematics

Physics (theoretical, computational, or statistical)

Quantum computing / quantum information

Algorithms-heavy computer science

Strong intuition and problem-solving ability matter more than labels.

Required skills:

Good mathematical foundation (linear algebra, probability, optimization)

Comfort with energy-based models (Ising, QUBO, loss landscapes)

Solid programming skills (Python and/or C++)

Ability to reason about algorithms beyond black-box usage

Nice to have

Experience with annealing methods, Monte Carlo techniques, or Ising models

Exposure to quantum annealers or Ising machines (hardware or simulators)

Research or R&D experience in optimization or computational physics