PhD Positions in the Theoretical Quantum Sciences
The Schilling Group is seeking outstanding and highly motivated PhD students and postdoctoral researchers in the theoretical quantum sciences with strong analytical and/or computational skills and scientific creativity.
Our research addresses fundamental challenges at the interface of quantum information theory, quantum chemistry, many-body physics, and quantum computing, with a particular focus on strongly correlated fermionic systems. We combine analytical and mathematical approaches with numerical methods within an ambitious and highly collaborative research environment.
Successful applicants will join an international and intellectually vibrant group working on frontier problems in the theoretical quantum sciences, including:
• P1: Functional theories for strongly correlated electrons (BeyondDFT)• P2: OpenMolcas – quantum chemistry code development• P3: Tensor network methods for quantum chemistry• P4: Foundations of quantum computing for fermionic systems• P5: Quantum information theory for fermions
The projects are partly funded by the ERC Consolidator Grant BeyondDFT.
The group is expected to relocate permanently to the University of Geneva on 1 September 2026.
Application Details:
• Application deadline: 3 June 2026• Expected start date: Flexible, ideally September 2026• Location: University of Geneva
Further information:
https://sites.google.com/view/christianschilling/open-positions
https://www.theorie.physik.uni-muenchen.de/17ls_th_nanophys_en/join_us1/schilling_group/index.html
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The structural evolution of the quantum technology sector necessitates a specialized layer of theoretical research focused on the fundamental behavior of correlated fermionic systems, a domain critical for the development of both quantum chemistry and condensed matter physics applications. As the ecosystem transitions from proof-of-concept hardware to early-stage utility, the ability to derive precise functional theories and tensor network methods serves as a vital bridge between abstract quantum mechanics and deployable algorithmic architectures. This role type addresses a persistent bottleneck in the software stack where the high-fidelity simulation of complex materials and molecules is currently limited by the computational scaling of classical methods. By advancing the mathematical foundations of fermionic quantum computing, these researchers ensure that the long-term trajectory of quantum advantage remains grounded in rigorous, reproducible scientific frameworks. Market signals indicate that organizations capable of integrating these deep theoretical insights into modular software toolchains will secure a significant competitive advantage in the emerging global quantum economy.
The quantum computing value chain is entering a phase of architectural maturation where the "application enablement" layer depends heavily on breakthroughs in many-body physics and quantum information theory. While hardware modalities continue to diversify, a critical systemic risk persists: the gap between the physical capabilities of current processors and the mathematical requirements for solving industrially relevant problems in chemistry and material science. This TRL mismatch is particularly acute for strongly correlated systems, where standard approximations like Density Functional Theory (DFT) often fail to provide the necessary accuracy for high-stakes R&D.
Global investment in quantum research, increasingly funneled through initiatives like the European Research Council (ERC), underscores the strategic importance of developing sovereign, high-performance quantum chemistry codes. These efforts are not merely academic; they represent the precursor to a transition where hybrid classical-quantum workflows become the standard for pharmaceutical and chemical engineering pipelines. However, the scalability of these solutions is currently impeded by a shortage of specialized talent capable of navigating the interface between quantum chemistry and tensor network mathematics.
Furthermore, the permanent relocation of high-level research groups to centers like the University of Geneva reflects a broader trend of ecosystem clustering. By centralizing expertise in fermionic systems and quantum network foundations, these hubs reduce the friction associated with cross-domain knowledge transfer. As public funding cycles increasingly prioritize "quantum-ready" workforce development, the role of the doctoral researcher evolves into a critical driver of the talent pipeline, ensuring that the next generation of industrial leadership possesses the analytical depth required to manage complex quantum-classical infrastructure.
The capability architecture for this role type centers on the intersection of advanced functional theories and the development of open-source quantum chemistry frameworks. Mastery of many-body phases and strongly correlated electron dynamics is essential for improving the precision of computational kernels, which in turn dictates the reliability of simulations on both NISQ and fault-tolerant hardware. This technical proficiency is coupled with the implementation of tensor network methods, a domain that provides the mathematical scaffolding necessary for reducing the dimensionality of complex quantum states without sacrificing physical accuracy.
These capabilities are critical for the structural throughput of the quantum software ecosystem because they define the limits of what programs can express and execute efficiently. By establishing the foundations of quantum computing specifically for fermionic systems, these researchers enable the standardization of interfaces between high-level chemical abstractions and low-level quantum gate sequences. This interface is vital for ensuring long-term interoperability across a fragmented vendor landscape, as it allows for the development of hardware-agnostic tools that can be calibrated for specific material science challenges.
Accelerates the deterministic progression of technology readiness for quantum-enhanced material science and chemical simulation
Mitigates systemic risks by establishing rigorous theoretical benchmarks for fermionic systems against classical high-performance baselines
Facilitates the transition from isolated mathematical proofs to standardized, open-source quantum chemistry software modules
Reduces iteration friction in drug discovery pipelines through the refinement of functional theories for strongly correlated electrons
Strengthens the long-term competitive positioning of regional quantum hubs by securing foundational expertise in many-body physics
Harmonizes abstract quantum information theory with the practical requirements of large-scale industrial computational workflows
Optimizes the lifecycle of hybrid quantum-classical systems by developing interoperable tensor network methodologies
Supports the scaling of quantum adoption by addressing the computational bottlenecks of traditional electronic structure methods
Shortens the time-to-market for quantum-ready research tools by aligning algorithmic development with hardware roadmap constraints
Improves the reliability of multi-institutional research initiatives through the application of standardized mathematical protocols
Protects capital-intensive deep-tech investments by providing expert validation of emerging quantum simulation paradigms
Enables the strategic orchestration of workforce development across international networks of academic and industrial partners
Industry Tags: Theoretical Quantum Science, Quantum Chemistry, Many-Body Physics, Fermionic Systems, Tensor Networks, Quantum Information Theory, BeyondDFT Research, University of Geneva Physics, ERC Funded Quantum Research, Quantum Algorithm Foundations
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