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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in Tensor Network methods and scientific programming, with a strong focus on developing and validating numerical simulations for quantum systems. Capable of collaborating across disciplines and contributing to scientific discussions and publications.
Highest-signal resume keywords
PhD In Physics, Mathematics, Computer Science, Or Quantum InformationTensor Network Methods (MPS, PEPS)Scientific Programming In Python, Julia, Or C++Classical Simulation Of Quantum Circuits Or AlgorithmsDevelopment Of Reusable Scientific Software
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Tensor Network MethodsNumerical SimulationsQuantum Noise ModellingEfficient Classical AlgorithmsQuantum CompilationScientific ProgrammingArchitecture EvaluationQuantum-Advantage StudiesDMRG TechniquesTEBD Techniques
Soft Skills
Scientific AutonomyCollaboration Across DisciplinesLiterature ReviewMethodology SelectionValidation Skills
Tools & Technologies
ITensorTeNPyQuimb
Industry Keywords
Quantum InformationClassical SimulationQuantum AlgorithmsPhotonic Quantum ComputingSpin-Photonic Quantum Computing
Tech Stack
Tools & technologiesPythonC++
About the role
Key responsibilities & impact- Apply Tensor Network methods to the classical simulation of quantum systems, circuits and architectures
- Investigate scientific questions, review relevant approaches and select suitable numerical methodologies
- Develop research-grade simulations adapted to Quandela’s hardware and future SPOQC architectures
- Study entanglement growth, simulation of complexity and the propagation of physical noise
- Explore different hardware configurations and architecture choices through numerical modelling
- Simulate quantum algorithms and contribute to comparisons with relevant classical approaches
- Develop reusable scientific code and document methods so they can support research across different teams
- Contribute to scientific discussions, publications and future research directions around classical simulation
- During the first 3 months, become familiar with Quandela’s architectures and research questions, investigate relevant methods and propose a first simulation approach
- Within 6–12 months, develop and validate methods on concrete problems, build reusable tools and take increasing ownership of projects across Device Physics and Quantum Information
- Longer term, become a key contributor to classical simulation at Quandela and extend the work toward future architectures, compilation or algorithm-level studies
Requirements
What you’ll need- PhD in Physics, Mathematics, Computer Science, Quantum Information or a closely related field, or equivalent research experience
- Practical experience with Tensor Network methods, such as MPS, PEPS or closely related approaches
- Experience developing or adapting numerical simulations for quantum systems, circuits or algorithms
- Ability to move from an open scientific question to literature review, methodology selection, implementation and validation
- Scientific programming skills in Python, Julia and/or C++
- Ability to assess the limits of a numerical method, including trade-offs between accuracy, computational cost and scalability
- Scientific autonomy and ability to collaborate across different research disciplines
- Professional English
- Classical simulation of quantum circuits or quantum algorithms
- Quantum noise modelling
- Efficient classical algorithms and benchmarking against classical baselines
- Quantum compilation and native-gate-aware simulation
- Development of reusable scientific software and collaborative code practices
- Experience working with hardware-specific constraints
- Experience with tools such as ITensor, TeNPy or Quimb
- DMRG, TEBD or other Tensor Network techniques beyond MPS / PEPS
- Quantum-inspired or dequantised algorithms
- Photonic or spin-photonic quantum computing
- Experience applying Tensor Networks to architecture evaluation or quantum-advantage studies
Benefits
Comp & perks- Work across Device Physics, Quantum Information, FTQC and potentially Quantum Algorithms
- Apply Tensor Network methods to concrete hardware and architecture questions
- Take scientific ownership of open research problems and progressively build a reusable simulation capability
- Contribute to publications, conferences and broader research directions as the scope develops
- Competitive compensation aligned with your experience and level of responsibility
- Company savings plan
- 100% health coverage through Alan
- Transport reimbursement or sustainable mobility bonus
- Swile meal vouchers
- Access to Gymlib