PhD positions (4 years) available at the University of GdaÅsk. We are seeking a motivated PhD student that is interested to work on quantum information and mathematical optimization in the group of Felix Huber (https://huberfe.github.io/). Topics they can expect to work on are: • approximation algorithms in QIT (e.g. ground state energies) • symmetry- and size reduction of polynomial optimization problems. • characterization of quantum codes and quantum capacities • characterization of quantum correlations, entanglement, nonlocality • machine learning for optimization problems This four-year position is part of the project “Mathematical Optimization in Quantum Information”, funded by the National Science Centre in Poland, and profits from vibrant quantum research in at the Institutes of Theoretical Physics and Informatics of U. GdaÅsk and the ICQT GdaÅsk. Candidates should have training or experience in the following areas: quantum information and computation, mathematical programming, combinatorial optimization, or coding theory. We appreciate a proactive personality with a good ability for cooperation, a methodological way of working, and proficiency in English. Programming experience (Python/Julia, Sage, GAP, semidefinite programming) is a plus. The positions are offered for 4 years. The candidate will join the doctoral school at U. GdaÅsk, receiving a stipend of approx. 5000 PLN net + salary of 3466 PLN gross (5000 PLN net + 5340 PLN gross after successful midterm evaluation) and travel funding. The position includes Polish social security coverage and health insurance. Applicants should send their applications (one pdf file containing: CV, motivation letter, diploma, contact details of two references, signed data processing statement, Msc thesis) with subject “NCN PhD 2026: Surname” to: felix.huber@ug.edu.pl Application deadline: 30.06.2026. More details on the recruitment process can be found at NCN. The starting date is negotiable, preferably 01.10.2026. We welcome applicants from all backgrounds, and promote a friendly, safe, and supporting team work environment.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The integration of advanced mathematical optimization within quantum information theory (QIT) represents a foundational structural requirement for transitioning from theoretical quantum advantage to verifiable industrial utility. As the global quantum ecosystem matures, the necessity for specialized researchers capable of bridging abstract algorithmic complexity with robust computational frameworks has become critical for resolving the "bottleneck of scalability." This role type functions as a high-leverage intellectual bridge, ensuring that emerging protocols for error correction, state characterization, and capacity limits are mathematically grounded and computationally viable. Market indicators from national technology strategies emphasize that such deep-tech academic initiatives are essential for mitigating the systemic risks of algorithmic inefficiency in high-compute sectors. By formalizing the mathematical structures underlying quantum correlations and capacities, this function secures the essential theoretical infrastructure required for long-term technological sovereignty and the realization of fault-tolerant systems.
The quantum information landscape is currently undergoing a decisive transition from exploratory research to the rigorous formalization of the software-hardware interface. While hardware modalities continue to diversify, the primary constraint for sector-wide advancement has shifted to the algorithmic layer, specifically regarding the optimization of resource-intensive processes such as quantum error correction and state characterization. Current ecosystem initiatives aim to accelerate readiness for practical quantum applications by focusing on the reduction of polynomial optimization problems and the refinement of approximation algorithms. This shift necessitates a high degree of mathematical sophistication to ensure that theoretical models remain compatible with the physical constraints of near-term and future quantum processors.
Sector-wide efforts continue to address talent and integration challenges by fostering deep-tech research pipelines that align with public funding cycles and international scientific mandates. The European quantum ecosystem, in particular, relies on the convergence of theoretical physics and informatics to drive the development of standardized benchmarking protocols. These academic-to-industrial translation pathways are critical for maintaining the momentum of the global value chain, as they provide the verifiable evidence required for capital allocation in the deep-tech sector. Furthermore, the increasing integration of machine learning within optimization frameworks indicates a structural move toward hybrid computational strategies that leverage both classical and quantum advantages.
Workforce scarcity remains acute at the intersection of combinatorial optimization, coding theory, and quantum information science. As organizations move beyond NISQ-era benchmarks, the ecosystem requires specialized practitioners who can navigate the fragmentation of the theoretical stack and the lack of unified software architectures. This structural layer of expertise is the primary mechanism for maintaining the high standards of reproducibility and methodological rigor necessary for the eventual deployment of scalable, fault-tolerant quantum services within global enterprise infrastructures.
The capability architecture for this specialized research domain centers on the synchronization of advanced mathematical programming with the emerging requirements of quantum systems engineering. Mastery of semidefinite programming, polynomial optimization, and symmetry reduction is essential for ensuring that quantum protocols are optimized for throughput and stability. These capabilities matter because they provide the necessary leverage to assess the true limits of quantum capacities and the robustness of error-correcting codes before physical implementation.
This role type facilitates critical interface points between theoretical complexity and the practical constraints of quantum compilers and hardware architectures. By establishing rigorous frameworks for characterizing quantum correlations and entanglement, researchers enable the parallelization of hardware development with the refinement of the algorithmic layer. Such expertise is fundamental to the interoperability of the quantum software value chain, as it provides the mathematical certainty needed to drive the standardization of architectural implementation protocols across disparate technology platforms. - Accelerates the transition from abstract quantum information theory to formalized computational optimization protocols
- Mitigates systemic research risks by providing mathematically rigorous characterizations of quantum capacities and codes
- Facilitates the structural reduction of complex polynomial optimization problems to enhance algorithmic efficiency
- Strengthens the reliability of the quantum software stack through the application of advanced approximation algorithms
- Reduces iteration friction between fundamental mathematical discoveries and the deployment of quantum protocols
- Optimizes the development of error-correction frameworks essential for the realization of fault-tolerant systems
- Enhances the stability of the global quantum value chain by establishing standardized benchmarks for entanglement
- Supports the scaling of quantum information systems by managing the complex dependencies of combinatorial optimization
- Improves the transparency of technology readiness level progression for stakeholders in the scientific community
- Enables the structural reproducibility of quantum experiments through the standardization of mathematical implementation
- Protects long-term research investments by ensuring alignment between theoretical innovation and computational scalability
- Orchestrates the convergence of academic research pathways with the practical demands of the quantum ecosystemIndustry Tags: Quantum Information Theory, Mathematical Optimization, Quantum Error Correction, Combinatorial Optimization, Semidefinite Programming, Algorithmic Complexity, Quantum Capacities, Deep Tech Research
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