Quantum computing offers a transformative approach to materials simulation, potentially performing quantum-mechanical calculations orders of magnitude faster and more accurately than classical methods on conventional high-performance computers. This unlocks the true computational design of novel materials, paving the way for solutions to global challenges related to sustainability and renewable energies. This capability can revolutionize various fields, enabling, for example, the improvement of catalytic processes and a deeper, atomistic understanding of material degradation. These advancements pave the way for knowledge-based optimization of load, process, and operating conditions in devices like fuel cells, ultimately reducing our reliance on scarce, environmentally harmful, and expensive materials. The focus of the PhD thesis is the development and testing of hybrid quantum-classical workflows and relevant models for simulating industrially relevant materials on a near-term quantum computer.
As a PhD candidate, you will:
Develop and implement hybrid quantum-classical algorithms and tools for materials simulation.
Design and conduct experiments to test and validate these algorithms on quantum computing platforms.
Collaborate with interdisciplinary teams to integrate quantum computing methods with classical simulation tools.
Analyze and interpret simulation results to gain insights into material properties and behaviors.
Document your research findings and contribute to scientific publications and presentations.
Participate in regular meetings and discussions to share progress and receive feedback.
Quantum computing offers a transformative approach to materials simulation, potentially performing quantum-mechanical calculations orders of magnitude faster and more accurately than classical methods on conventional high-performance computers. This unlocks the true computational design of novel materials, paving the way for solutions to global challenges related to sustainability and renewable energies. This capability can revolutionize various fields, enabling, for example, the improvement of catalytic processes and a deeper, atomistic understanding of material degradation. These advancements pave the way for knowledge-based optimization of load, process, and operating conditions in devices like fuel cells, ultimately reducing our reliance on scarce, environmentally harmful, and expensive materials. The focus of the PhD thesis is the development and testing of hybrid quantum-classical workflows and relevant models for simulating industrially relevant materials on a near-term quantum computer.
As a PhD candidate, you will:
Develop and implement hybrid quantum-classical algorithms and tools for materials simulation.
Design and conduct experiments to test and validate these algorithms on quantum computing platforms.
Collaborate with interdisciplinary teams to integrate quantum computing methods with classical simulation tools.
Analyze and interpret simulation results to gain insights into material properties and behaviors.
Document your research findings and contribute to scientific publications and presentations.
Participate in regular meetings and discussions to share progress and receive feedback.
See PhD position offering at Bosch Research
