Applied Research Scientist - Probabilistic Computing

Quantum Dice; Oxford; On-site; GBP 90,000 - 120,000; Full time

Job summary

Quantum Dice in Oxford seeks an Applied Research Scientist to develop algorithms for entropy-driven probabilistic computing, focusing on p-dits and Gaussian units and aligning software with hardware roadmaps. You will move from theory to Python simulators, translate designs into hardware-agnostic pseudo-code, and work with the hardware team to ensure efficient implementation on Quantum Dice’s architecture.

This role emphasizes rigorous benchmarking, integration with industrial problems, and

Qualifications

• PhD preferred in Physics, CS, Applied Mathematics, or related field.
• Familiarity with probabilistic algorithms and MCMC methods (Gibbs sampling).
• Knowledge of Bayesian inference and Boltzmann machines.
• Ability to translate algorithms into hardware-agnostic pseudo-code.
• Experience with Python for simulator verification.

Responsibilities

• Research and extend Adaptive Parallel Tempering and Simulated Quantum Annealing.
• Investigate p-dits and Gaussian units within optimisation frameworks.
• Develop Boltzmann machines and Bayesian learning frameworks.
• Automate parameter prediction and self-optimising loops.

Skills

Probabilistic algorithms

MCMC methods

Gibbs sampling

Bayesian inference

Python

Pseudo-code translation

Education

PhD (preferred) in Physics, CS or Applied Mathematics

Tools

FPGA/ASICs

Ising models

QUBO

Hardware-aware design

Job description

Quantum Dice in Oxford seeks an Applied Research Scientist to develop algorithms for entropy-driven probabilistic computing, focusing on p-dits and Gaussian units and aligning software with hardware roadmaps. You will move from theory to Python simulators, translate designs into hardware-agnostic pseudo-code, and work with the hardware team to ensure efficient implementation on Quantum Dice’s architecture.

This role emphasizes rigorous benchmarking, integration with industrial problems, and