Applied Research Associate

As an Applied Research Scientist in probabilistic computing, you will work on the development and refinement of algorithms that leverage the unique entropy-driven capabilities of our PPU. You will move beyond binary logic, investigating how p-dits and Gaussian units are used to outperform traditional CPU/GPU architectures and quantum annealers.

This is a 'full-stack' research role which means that you will move from mathematical theory to simulator verification in Python, as well as creating pseudo-code specifications for our hardware engineering team. A core part of this role involves understanding the specific physical nature of Quantum Dice’s hardware to ensure that algorithmic development is perfectly aligned with our hardware roadmaps.

• Research and develop novel extensions to Adaptive Parallel Tempering (APT), Simulated Quantum Annealing (SQA) and similar algorithms, and implementing new algorithmic paradigms that move beyond traditional simulated annealing.
• Investigate the use of p-dits and Gaussian units within optimisation frameworks to improve convergence and solution quality.
• Develop Boltzmann machines and Bayesian learning frameworks specifically geared toward causal and explainable AI, ensuring transparency in complex model outputs.
• Continuous improvement of automated parameter prediction system, creating self‑optimising loops that allow the submission script to adapt to problem‑specific landscapes without manual intervention.

• Define rigorous performance metrics and plan comprehensive test suites for industrial-scale problems (e.g. logistics, finance, or materials science).
• Work on the integration of probabilistic kernels into automated decision‑making engines.
• Conduct competitive benchmarking against state‑of‑the‑art classical solvers and quantum backends.

• Maintain and extend our Python‑based simulators to verify algorithmic performance.
• Translate research into pseudo‑code for hardware implementation. You will learn the specifics of how algorithms are physically implemented on Quantum Dice’s architecture to ensure your designs are hardware‑efficient.

• PhD (preferred) or a research‑heavy MSc in Physics, Computer Science, Applied Mathematics or a related field.
• Familiarity with probabilistic algorithms, ideally also having implemented MCMC methods, Gibbs sampling, and energy‑based models.
• Familiarity with Bayesian inference, Causal AI, and the mathematical foundations of Boltzmann machines.
• Ability to review Python code and translate algorithms into hardware‑agnostic pseudo‑code.

• Experience with hardware‑aware algorithm design (e.g., FPGAs, ASICs, or photonic circuits).
• Knowledge of combinatorial optimization (Ising models, QUBO) and its application in industrial decision-making.
• Previous experience in a deep‑tech startup environment.

• It's an exciting time to work in probabilistic computing and you’ll be defining the libraries for an entirely new class of computer.
• We maintain strong ties to the University of Oxford, offering a vibrant intellectual environment and access to world‑leading experts.
• Our technology targets critical real‑world sectors, including logistics, drug discovery, and climate modelling.
• We are a diverse team of passionate thinkers meeting builders. We value curiosity, transparency and a good sense of humour.

Quantum Dice is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.