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Master's programmesThe Division is responsible for two master's programmes in Material Science and X-ray and Neutron Science. These programmes are made in connection with the two major multidisciplinary research facilies in Lund: MAX-IV laboratory and European Spallation Source.Master's programme in Materials Science (Lund University's website)Master's programme in X-ray and Neutron Science (Lund
https://www.sljus.lu.se/education - 2026-09-29
With nanotechnology, our researchers can create very small structures. The technology enables the development of materials that hold promise for future advances in fields such as information technology, sustainability and precision medicine. When a material becomes very small, it can acquire entirely new properties. On the nanoscale, a substance can behave in a radically different way than when it
https://www.lunduniversity.lu.se/research-and-innovation/focus-areas/strategic-research-areas/nanoscience - 2026-09-29
As a PI, postdoc, staff, or student associated with NanoLund, you have access to a wide range of support designed to strengthen your research and career. It can be funding, visibility, access to labs and equipment, meetings, or download of templates and pictures. Read more about membership, what support you can get, and how to access it. Meetings & SeminarsNanoLund FundingGuidelines for Acknowledg
https://www.nano.lu.se/nanolundians - 2026-09-29
Our researchers meet society's challenges in IT and communications. Together with the industry, they are building the information technology of the future through new innovations, methods and knowledge. The development of and demand for 6G technology, robots and self-driving cars is advancing rapidly. In these areas, technological breakthroughs are crucial if we are to meet the needs of the indust
https://www.lunduniversity.lu.se/research-and-innovation/focus-areas/strategic-research-areas/information-technology - 2026-09-30
Despite its simplicity and relevance for the description of electronic correlations in solids, the Hubbard model is seldom inarguably realized in real materials. Here, we show that monolayer Nb3Cl8 is an ideal candidate to be described within a single-orbital Hubbard model, constructed within a “molecular” rather than atomic basis set using ab initio constrained random phase approximation calculat
https://www.iiiee.lu.se/education/doctoral-programme - 2026-09-29
Quantum mechanics forbids perfect discrimination of non-orthogonal states, a limitation that can be exploited to certify intrinsic randomness. Building on this principle, this thesis investigates how limits on state discrimination can be used to generate certified randomness in prepare-and-measure protocols. In particular, we study how the performance of minimum-error quantum state discrimination
Visiting Professor at the Lund University School of Economics and Management Current position: ProfessorCurrent university or institution: Columbia UniversityCountry: USA Why did you choose Lund University?I have already spent two years as a visiting professor in the Department of Statistics at LUSEM, which has been an extremely positive experience. During this time, I have established strong co
https://www.lunduniversity.lu.se/iuliana-ionita-laza - 2026-09-30
We use a numerical model based on non-equilibrium Green's functions to investigate the influence of interface roughness (IFR) scattering in terahertz quantum cascade lasers. We confirm that IFR is an important phenomenon that affects both current and gain. The simulations indicate that IFR causes a leakage current that transfers electrons from the upper to the lower laser state. In certain cases,
In a quantum-mechanical system, particle-hole duality implies that instead of studying particles, we can get equivalent information by studying the missing particles, the so-called holes. Using this duality picture for fermions in a rotating trap the vortices appear as holes in the Fermi sea. Here we predict that the formation of vortices in quantum dots at high magnetic fields causes oscillations
Inverse problems are important in quantum mechanics and involve such questions as finding which potential give a certain spectrum or which arrangement of atoms give certain properties to a molecule or solid. Inverse problems are typically very hard to solve and tend to be very compute intense. We here show that neural networks can easily solve inverse problems in quantum mechanics. It is known tha
