Website:
amunra.io
Job details:
The Role
Amunra is seeking exceptional Quantitative Researchers with PhDs in mathematically rigorous scientific disciplines.
This is not a conventional quantitative-finance research role.
We are particularly interested in scientists trained to reason about high-dimensional interacting systems, stochastic processes, nonlinear dynamics, emergence, collective behaviour, critical phenomena, networks, information, scaling, and systems far from equilibrium.
Researchers will investigate fundamental questions about the structure and dynamics of financial markets and translate scientific findings into rigorous quantitative methodologies and computational systems.
The role sits at the intersection of fundamental research, applied mathematics, computational science, and quantitative finance.
Researchers will have substantial freedom to formulate hypotheses, develop mathematical frameworks, design numerical experiments, work with large empirical datasets, and contribute to Amunra's proprietary research programme.
Research Areas
Depending on background and expertise, research may involve:
- Complex adaptive systems
- Statistical mechanics and non-equilibrium systems
- Interacting stochastic systems
- Nonlinear and stochastic dynamics
- Critical phenomena and phase transitions
- Scaling laws and universality
- Multifractal and multiscale systems
- Long-memory and anomalous diffusion
- Random matrix theory
- Information theory and statistical inference
- Network science and interacting networks
- Collective behaviour and emergence
- Dynamical stability and instability
- Stochastic processes and stochastic differential equations
- High-dimensional statistical systems
- Spatiotemporal systems
- Endogenous dynamics and interacting agents
- Computational modelling and simulation
- Machine learning for scientific discovery
- Representation learning for complex systems
- Inference in noisy and non-stationary systems
Financial markets will serve as the principal empirical domain in which these ideas are developed, tested, and applied.
What You Will Do
You will:
- Formulate original research questions concerning complex financial systems
- Develop mathematical, statistical, and computational models of interacting market phenomena
- Identify measurable structure in large, noisy, high-dimensional, and non-stationary datasets
- Investigate dynamics across multiple timescales and levels of aggregation
- Study nonlinear dependence, collective behaviour, structural change, and emergent phenomena
- Develop novel quantitative measures and mathematical representations
- Formulate hypotheses from first principles and subject them to rigorous empirical testing
- Distinguish genuine structure from statistical artefacts, spurious relationships, and overfitting
- Design numerical experiments, simulations, and computational research pipelines
- Work with large-scale financial datasets and high-performance research infrastructure
- Collaborate across physics, mathematics, machine learning, quantitative finance, and engineering
- Translate successful research into robust computational methodologies
- Contribute to Amunra's proprietary scientific research and intellectual property
Researchers are expected to challenge established assumptions and develop new approaches where conventional methodologies are insufficient.
Required Qualifications
A PhD is mandatory.
We are particularly interested in doctoral backgrounds including:
- Theoretical Physics
- Statistical Physics
- Mathematical Physics
- Quantum Field Theory
- Condensed Matter Physics
- Complex Systems / Complexity Science
- Statistical Mechanics
- Nonlinear Dynamics
- Dynamical Systems
- Applied Mathematics
- Probability and Stochastic Processes
- Network Science
- Information Theory
- Computational Physics
- Computational Science
- Fluid Dynamics / Turbulence
- Quantum Information
- Mathematical or Computational Biology involving complex systems
- Other closely related mathematically intensive disciplines
Exceptional candidates from adjacent fields may be considered where their doctoral research demonstrates significant mathematical, statistical, or computational depth.
Scientific Profile
Strong candidates will typically demonstrate several of the following:
- Deep mathematical maturity
- Experience studying systems with many interacting degrees of freedom
- Strong foundations in probability, statistics, and stochastic processes
- Experience with nonlinear, non-equilibrium, or high-dimensional systems
- Ability to move between theoretical reasoning and empirical investigation
- Experience extracting structure from noisy datasets
- Familiarity with numerical methods and computational modelling
- Ability to formulate models from first principles
- Comfort working beyond assumptions of equilibrium, stationarity, independence, and linearity
- Strong scientific skepticism and experimental discipline
- Evidence of original research rather than solely implementation of established techniques
Publication quality, originality, and intellectual depth matter more to us than publication count.
Computational Skills
Candidates should be comfortable conducting computational research independently.
Strong proficiency in Python or an equivalent scientific-computing language is expected.
Experience with some of the following is advantageous:
- NumPy / SciPy
- PyTorch or JAX
- Numerical optimisation
- Monte Carlo methods
- Statistical computation
- Time-series analysis
- Graph and network computation
- Parallel and high-performance computing
- C / C++ / Julia
- GPU computing
- Large-scale datasets and distributed computing environments
Researchers are not expected to be software engineers, but they must be capable of implementing and rigorously testing their own ideas.
Financial Experience
Prior experience in finance is not required.
Amunra is deliberately interested in researchers capable of bringing mathematical tools, scientific methods, and intellectual traditions from outside conventional quantitative finance.
Candidates with financial-market experience are welcome, but scientific depth takes precedence over familiarity with standard financial models.
Researchers entering from physics, mathematics, or adjacent sciences will be expected to develop a rigorous understanding of financial markets as an empirical system.
What We Are Not Looking For
This role is unlikely to be suitable for candidates whose experience is primarily in:
- Conventional equity factor modelling
- Standard financial econometrics without broader scientific research depth
- Discretionary investment research
- Fundamental equity research
- Routine implementation of established quantitative strategies
- Dashboard analytics or business intelligence
- Generic data science
- Pure signal mining without a deeper scientific hypothesis
Amunra is seeking researchers interested in understanding the structure and dynamics of markets, rather than simply fitting predictive models to financial time series.
Research Culture
Amunra is being built as a deliberately interdisciplinary research environment.
A theoretical physicist may work alongside a statistical physicist, complexity scientist, applied mathematician, network scientist, machine-learning researcher, quantitative researcher, engineer, and experienced market practitioner.
We are interested in ideas that cross disciplinary boundaries—but only when they survive rigorous mathematical and empirical scrutiny.
Researchers are expected to communicate across disciplines, challenge assumptions, and maintain exceptionally high standards of scientific evidence.
Intellectual independence is encouraged. Scientific rigor is mandatory.
Candidate Standard
We expect this role to be highly selective.
We look for:
Scientific depth — A serious command of your doctoral field.
Originality — Evidence that you have developed ideas, not merely applied existing methods.
Mathematical rigor — Comfort reasoning formally about difficult systems.
Empirical discipline — The ability to separate compelling narratives from statistically defensible results.
Computational ability — The capacity to turn theoretical ideas into reproducible numerical experiments.
Intellectual range — An interest in learning across physics, mathematics, computation, and financial markets.
Research ambition — A desire to work on questions for which the methodology may not yet exist.
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