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 usedto 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.

You will work on:

1) Algorithm

• Research and develop novel extensions toAdaptive Parallel Tempering (APT),Simulated Quantum Annealing (SQA) and similar algorithms + implementingnew algorithmic paradigms that movebeyondtraditional simulated annealing.


• Investigate the use ofp-ditsandGaussian units within optimisation frameworks to improve convergence and solution quality.


• DevelopBoltzmann machinesandBayesian 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.

2) Benchmarking and industrial integration

• 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.

3) Hardware‑algorithm co‑design

• Maintain and extend our Python‑based simulatorsto verify algorithmic performance.


• Translate research intopseudo‑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.

Who you are:

• 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.

Nice to have:

• 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.

Why join us?

• 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.