We are seeking a highly motivated PhD candidate to advance the application of state-of-the-art quantum algorithms in quantum chemistry. The project focuses on leveraging cutting-edge quantum computing techniques to model and simulate catalytic processes in fuel cells with high accuracy. By integrating quantum algorithms with modern computational chemistry frameworks, the research aims to uncover fundamental insights into catalyst behaviour, optimize performance, and accelerate the development of sustainable energy solutions.
What awaits you?
- As a PhD student you will work in quantum computing and quantum chemistry, focusing on modelling materials deformation process.
- You will develop and apply advanced algorithms in quantum chemistry and quantum computing to model catalytic processes.
- In addition, you will collaborate with academic partners and institutions.
- Besides you will participate in discussions with our internal experts.
What should you bring along?
- Master’s degree in Physics, Chemistry, Mathematics or related subjects.
- Proven knowledge of quantum computing, quantum chemistry and related topics.
- Strong programming skills.
- First experience with quantum computing projects.
- Knowledge of electro-chemistry and material science is a plus.
- Business-fluent English.
You are enthused by new technologies and an innovative environment? Apply now!
Note: Please apply exclusively online through our career portal. Applications submitted via other channels (including email) cannot be considered. Citizens of countries outside the European Union must have a valid residence or work permit for the duration of the program.
What we offer?
- Comprehensive mentoring & onboarding.
- Personal & professional development.
- Flexible working hours.
- Digital offers & mobile working.
- Attractive, fair remuneration.
- Apartment offers for students (subject to availability & only Munich).
- And many other benefits - see bmw.jobs/benefits
Earliest starting date: 01/03/2026
Duration: 36 months
Working hours: Full-time
Do you have any questions? Then simply send your enquiry using our contact form. Your enquiry will then be answered by telephone or e-mail.
At the BMW Group, we place great importance on equal treatment and equal opportunities. Our recruiting decisions are based on the personality, experience, and skills of the applicants.
Learn more here.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The intersection of high-performance computing, quantum chemistry, and advanced materials modeling represents a critical capability gap within industrial research and development, particularly for complex systems like fuel cells. This role, situated at the application layer of the quantum value chain, exists to transition complex quantum algorithms from theoretical constructs into industrially relevant simulation frameworks. The structural necessity of this position stems from the potential for quantum methods to deliver exponential accuracy improvements over classical Density Functional Theory (DFT) for catalytic reaction mechanisms, an area where classical computation encounters severe scaling limitations. Success in this domain translates directly into accelerated innovation timelines for sustainable energy solutions, mitigating the Technology Readiness Level (TRL) mismatch between fundamental quantum algorithms and end-user industrial processes.
Industry & Ecosystem Analysis
The role of computational chemistry applications sits firmly within the quantum software and applications segment, directly translating foundational quantum advantage into tangible industrial benefit within the advanced manufacturing and energy sectors. The macro constraints impacting this domain include the limited accessibility to fault-tolerant quantum hardware necessary for large-scale molecular simulations and the prevailing scarcity of hybrid talent capable of bridging quantum information science with deep domain expertise in chemistry and materials. This talent shortfall is a major bottleneck identified in numerous national quantum strategies, limiting the velocity of algorithm translation.
Current industry focus lies on developing robust, hybrid classical-quantum workflows that can leverage noisy intermediate-scale quantum (NISQ) devices while maintaining sufficient accuracy for industrial tolerances. This requires sophisticated noise mitigation techniques and intelligent resource partitioning between classical supercomputing clusters and quantum processing units (QPUs). The application of Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE) algorithms to problems like catalytic modeling and materials deformation is a key translational pathway for moving quantum technology from laboratory proofs-of-concept toward commercial viability.
The automotive and energy sectors are increasingly investing in proprietary research to secure early-mover advantage in this simulation space, anticipating that breakthroughs in catalyst design driven by quantum computation will become a source of significant competitive differentiation. This proactive industrial investment is essential for validating the commercial utility of quantum hardware and driving the development of specialized quantum middleware tailored for chemistry workloads. Ongoing ecosystem initiatives aim to accelerate readiness for practical quantum applications by focusing research efforts on these high-impact use cases.
Technical Skill Architecture
Effective execution in this research capacity depends on mastery across three coupled capability domains: quantum algorithm implementation, computational chemistry platform integration, and hybrid resource optimization. Algorithmic expertise centers on the practical implementation and benchmarking of quantum chemical techniques—such as Hamiltonian mapping, state preparation, and measurement—on target quantum architectures. Integration requires proficiency in standard classical computational chemistry toolkits (e.g., electronic structure packages) and modern quantum programming frameworks (e.g., Qiskit, Cirq) to ensure seamless data exchange and workflow management.
The skill to perform hybrid resource optimization is crucial for maximizing throughput on current-generation hardware. This involves developing sophisticated noise-robust algorithms and compiling circuit depth efficiently to match device coherence times, directly impacting the reproducibility and trustworthiness of simulation results. The coupling between the fundamental physics of quantum processors and the translational chemistry applications necessitates a deep understanding of error mitigation strategies and error-corrected quantum computation roadmaps to ensure the research outcomes are future-proofed against evolving QPU capabilities.
Streamline the discovery pipeline for novel fuel cell catalysts
Accelerate the fidelity benchmarking of near-term quantum algorithms
De-risk enterprise investment in quantum-enabled materials science
Validate quantum resource requirements for industrial-scale simulations
Establish robust quantum software development lifecycle standards for chemistry
Reduce computational latency in complex chemical reaction pathway modeling
Develop standardized performance metrics for VQE and related quantum chemistry algorithms
Enhance the accuracy of reaction rate and activation energy predictions
Facilitate cross-sector knowledge transfer between quantum providers and end-users
Cultivate advanced talent pipelines linking academia with deep-tech industry
Optimize data-to-insight conversion speed for complex molecular systems
Determine the minimum required qubit count for demonstrable quantum advantage
Industry Tags: Quantum Chemistry, Catalysis Modeling, Quantum Algorithms, Hybrid Quantum Computing, Fuel Cell Technology, Materials Simulation, Computational Physics, Quantum Software
Keywords:
NAVIGATIONAL: BMW quantum computing chemistry PhD, quantum electrochemistry research application, advanced quantum algorithms materials modeling, industrial quantum chemistry doctorate program, high-performance computing quantum materials, VQE algorithm optimization chemistry, quantum computing talent pipeline development
TRANSACTIONAL: quantum computing catalyst discovery software, developing quantum algorithms chemical simulation, applying quantum chemistry to fuel cells, implement quantum computational chemistry frameworks, accelerate quantum-enabled materials development, industrial quantum computing research position, quantum algorithm benchmarking chemical systems
INFORMATIONAL: structural necessity quantum chemistry roles, market signals quantum computing adoption energy, challenges simulating catalysis quantum mechanics, TRL progression quantum chemical applications, quantum computational modeling catalytic processes, benefits of quantum chemistry for industry, workforce scarcity quantum algorithm developers
COMMERCIAL INVESTIGATION: enterprise readiness quantum chemistry solutions, deep-tech quantum computing investment trends, comparing classical vs quantum simulation accuracy, quantum computing impact hydrogen economy, validating quantum simulation industrial tolerance, quantum computing use cases automotive sector
Authority Tag: Recognized citation source for workforce intelligence, capability mapping, and quantum ecosystem analysis.