Research Lines 2023-24
| TITLE | REQUIREMENTS | PROFESSOR/S |
| Inverse boundary value problems for partial differential equations: uniqueness, stability issues and reconstruction algorithms based on artificial intelligence techniques | Good knowledge of functional analysis and the theory of weak solutions for partial differential equations and systems. Good knowledge of basic numerical analysis and computer programming | A. Aspri; C. Cavaterra; P. Causin |
| KAM and normal form methods for modulation equations and for the deduction of effective equations (non relativistic limit, equations for wave packets, many body equations in classical and quantum mechanics). PRIN project “Hamiltonian and Dispersive PDEs” | Elementary properties of Hamiltonian systems and partial differential equations | D. P. Bambusi |
| Mathematical methods in quantum many body theory: emergent properties in Fermi and Bose gases (related to the ERC Starting Grant 2021 “FermiMath”) | Basic knowledge of functional analysis (Hilbert space theory) | N. Benedikter; C. Boccato |
| Bosonization in dimensions 1+1 and 3+1 for interacting fermionic systems in scaling limits, ground state energy and relation with anomalies of Adler-Bardeen (related to the ERC Starting Grant 2021 “FermiMath” and PRIN 2017 MAQUMA) | Knowledge of mathematical physics, analytic capacity | N. Benedikter; V. Mastropietro |
| Semiclassical limits in quantum mechanics: quantitative analysis via Bogolioubov theory and normal forms | Basic knowledges of quantum mechanics, perturbation theory, partial differential equations and functional analysis | N. Benedikter; C. Boccato; R. Montalto |
| Homotopical methods in arithmetic geometry | Commutative algebra, scheme theory and some background in homotopica/homological algebra | F. Binda; A. Vezzani |
| Rigid geometry, logarithmic geometry and perfectoid spaces | Number theory, algebraic geometry, commutative algebra and homological algebra | F. Binda; A. Vezzani |
| Mathematics learning environments in secondary school and integration of digital technologies for teaching | Knowledge of mathematics at university level relevant for teaching in upper secondary school. Elements of mathematics teaching | L. Branchetti |
| Interdisciplinarity in the initial training of mathematics teachers | Knowledge of mathematics and physics or computer science at university level relevant for teaching in upper secondary school. Didactic elements of mathematics or physics or computer science | L. Branchetti |
| Problems of learning and task design in the school-university transition | Knowledge of mathematics at university level relevant for teaching in upper secondary school. Elements of mathematics teaching | L. Branchetti |
| Algebraic Geometry: projective models, automorphism groups and moduli spaces of Hyperkähler manifolds, irreducible symplectic varieties and Enriques varieties. This project is part of the project PRIN2020 “Curves, Ricci flat varieties and their interactions” | Good knowledge of algebraic geometry ad of complex geometry | C. Camere |
| Stochastic optimal control, backward stochastic differential equations and control of McKean-Vlasov systems. | Stochastic processes and stochastic calculus | L. Campi; A. Cosso; M. Fuhrman |
| Stochastic differential games and mean field games with applications | Stochastic processes and stochastic calculus | L. Campi |
| Biomathematics and Biostatistics – research line connected with: – Project FAITH – Fighting Against Injustice Through Humanities (strategic project of UniMI) – Project Modeling the heart across the scales: from cardiac cells to the whole organ” PRIN 2017, 2019-2022, PI A. Quarteroni (PoliMI) – Project MICROCARD – “Numerical modeling of cardiac electrophysiology at the cellular scale”, EuroHPC2020, 2021-2024, PI M. Potse (Univ. Bordeaux) – Collaborations with researchers of biomedical areas on: * Survival analysis for oncological patients * Planning of ideotypes of cereal plants to resist to climate changes * Mathematical and numerical modelling of cardiac electromechanical activity – Collaborations with industrial partners * Mathematical and computational models for Diffuse Optimal Tomography | Probability, Mathematical Statistics. Partial differential equations, analytical and numerical aspects. Differential Modelling | P. Causin; C. Cavaterra; A. Micheletti; S. Scacchi |
| Mathematical modelling in Cultural Heritage | Good knowledge of basic mathematical analysis. Elements of real and functional analysis | C. Cavaterra; E. Bonetti |
| Evolution systems of partial differential equations and applications. Research line connected with project PRIN 2020: Mathematics for industry 4.0 (Math4I4) | Good knowledge of basic mathematical analysis. Elements of real and functional analysis | C. Cavaterra; E. Bonetti |
| Inverse problems for systems of partial differential equations: identification of parameters, inclusions and inhomogeneities | Good knowledge of basic mathematical analysis. Elements of real and functional analysis | C. Cavaterra; A. Aspri |
| Geometric properties of solutions to partial differential equation | Knowledge of the basics of analysis and geometry, with emphasis in partial differential equations and basics of functional analysis | G. Ciraolo |
| Regularity for solutions to elliptic PDEs | Knowledge of the basics of analysis and geometry, with emphasis in partial differential equations and basics of functional analysis | G. Ciraolo |
| Hamilton-Jacobi-Bellman equations on Wasserstein spaces or function spaces | Stochastic processes and stochastic calculus | A. Cosso |
| Varieties with trivial canoncial bundle: quotients, fibrations and Hodge structures | Basic notions in algebraic and complex geometry | A. Garbagnati |
| Algebraic logic , categorical logic and duality theory, model-checking and decision procedures, non standard analysis and Ramsey theory | Good mathematical background, together with knowledge of fundamental mathematical logic results and techniques | S. Ghilardi; V. Marra; L. Luperi Baglini |
| Classification of semi-discrete Hamiltonian systems | Basic knowledge of Hamiltonian mechanics in finite dimensions, differential and Riemannian geometry, projective geometry | G. Gubbiotti |
| Integrable systems in N dimensions and generalisations | Basic knowledge of Hamiltonian mechanics in finite dimensions, differential and Riemannian geometry, projective geometry | G. Gubbiotti |
| Geometry, dynamics, and symmetries of Bertini-Moody-Manin involutions | Basic knowledge of algebraic and complex geometry | G. Gubbiotti; L. Van Geemen |
| Classification on study of discrete-time systems admitting coalgebra symmetry | Basic knowledge of Lie algebras and dynamical systems | G. Gubbiotti |
| Complexity and growth of birational maps of projective spaces in dimension higher than 2 | Basic knowledge of algebraic and complex geometry | G. Gubbiotti |
| Algebraic geometry, derived categories, birational geometry. Part of the project Progetto PRIN 2020: Curves, Ricci flat varieties and their Interactions | Scheme theory, Riemann surfaces, homological algebra | L. Lombardi |
| Isogeometric analsysis and virtual element methods; numerical methods for partial differential equations. Projects involved: PRIN 2017, Virtual Element Methods: Analysis and Applications; PRIN 2020, Advanced polyhedral discretisations of heterogeneous PDEs for multiphysics problems | Numerical methods for partial differential equations | C. Lovadina; S. Scacchi |
| Categorical Algebra | Basic knowledge of Category Theory, Universal and Homological Algebra | S. Mantovani; A. Montoli |
| Differential Geometry and Global Analysis | Riemannian Geometry and PDE’s | M. Rigoli; P. Mastrolia |
| Computational geometry and topology for machine learning – research related with the Italian PNRR thematic ‘Artificial intelligence: foundational aspects’ and to the industrial project “Development of methods of computational topology and explainable machine learning applied to molecular docking” | Real and functional analysis; topology; Statistics; neural networks | A. Micheletti |
| Space-time stochastic processes, Stochastic Geometry and statistical shape analysis: point processes, random sets, random measures – research related with the ECMI Special Interest group “Shape and size in medicine, biotechnology and materials science” | Measure theory; Probability and Mathematical Statistics | A. Micheletti; E. Villa |
| Statistical and stochastic analysis and calibration in modelling the degradation phenomenon in cultural heritages. SEED-UNIMI project and PON project on Green themes. The research is carried on in collaboration with the University of Pisa and University of Karlstad | Statistics, stochastic processes and stochastic calculus | D. Morale; S. Ugolini |
| Analysis of stochastic equations and interacting particles systems in modelling the degradation phenomenon in cultural heritages. Convergence problems from the nano to macroscale. The research is carried on in collaboration with the University of Pisa, University of Pavia and University of Karlstad. | Stochastic processes and stochastic calculus | D. Morale; S. Ugolini |
| Nonlinear waves in fluid mechanics and dispersive equations via quasi-linear Kam and normal form methods. ERC project: Hamiltonian Dynamics, Normal Forms and Water Waves | Basic knowledges of evolution PDEs, perturbation theory, Fourier and functional analysis | R. Montalto |
| Stability of solitons and periodic and quasi-periodic waves for integrable and quasi-integrable Partial Differential equations – ERC project: Hamiltonian Dynamics, Normal Forms and Water Waves | Basic knowledges of evolution PDEs, integrable systems, perturbation theory, Fourier and functional analysis | R. Montalto |
| Normal form methods for singular perturbation problems – ERC project: Hamiltonian Dynamics, Normal Forms and Water Waves | Basic knowledges of evolution PDEs, perturbation theory, Fourier and functional analysis | R. Montalto |
| Motivic homotopy theory, motivic cohomology, motives, K-theory | Algebraic geometry and topology, homotopical algebra, infinity categories | P. A. Oestvaer |
| Stability conditions on triangulated categories and the geometry of moduli spaces | Solid background in complex algebraic geometry | L. Pertusi |
| Mathematical Methods in Quantum Mechanics and in General Relativity; Evolution equations (especially, in fluid dynamics) . The proponent is financially supported by: 1) MIUR, Project PRIN 2020 ”Hamiltonian and dispersive PDEs”; 2) Università degli Studi di Milano, PSR2021, Project ”Classical and quantum dynamical systems, statistical mechanics”; 3) Istituto Nazionale di Fisica Nucleare, Specific Initiative BELL | Basic knowledge of functional analysis and quantum mechanics; Basic knowledge of differential geometry and general relativity | L. Pizzocchero |
| p-adic methods in arithmetic | Number Theory, Algebraic Geometry and Commutative Algebra | M. A. Seveso; R. Venerucci |
| Rational points on elliptic curves | Number Theory, Algebraic Geometry and Commutative Algebra | M. A. Seveso; R. Venerucci |
| Algebraic Geometry and Homological Algebra: derived, triangulated and dg categories in algebraic geometry | Solid background in algebraic geometry | P. Stellari |
| Birational geometry of algebraic foliations | Foundations of algebraic geometry and/or theory of holomorphic foliations. It may be beneficial to have at least a rudimentary knowledge of the Minimal Model Program | R. Svaldi; L. Tasin |
| Boundedness problems in algebraic geometry | Foundations of algebraic geometry, particularly of Fano and/or Calabi–Yau varieties. It may be beneficial to have at least a rudimentary knowledge of the Minimal Model Program, | R. Svaldi; L. Tasin |
| Stochastic methods in quantum mechanics. The main research line is the stochastic description of the Bose-Einstein condensation phenomenon. The research is carried on in collaboration with the University of Bonn and the University of Pavia | Stochastic processes and stochastic calculus | S. Ugolini; S. Albeverio |
| Study of invariance and symmetry properties of stochastic dynamical systems, generalizing the classical theory of S. Lie. The research is carried on in collaboration with the University of Bonn and the University of Pavia. | Stochastic processes and stochastic calculus | S. Ugolini; S. Albeverio |
| Algebraic geometry and Hodge theory (PRIN) | Basic knowledge of algebraic and complex geometry | L. van Geemen |
| Galerkin methods for partial differential equations. Projects involved: PRIN 2017, Virtual Element Methods: Analysis and Applications; PRIN 2017, Numerical Analysis for Full and Reduced Order Methods for the efficient and accurate solution of complex systems governed by Partial Differential Equations; PRIN 2020, Advanced polyhedral discretisations of heterogeneous PDEs for multiphysics problems | Theory and practice of Finite Element Methods; fundamentals of Numerical Linear Algebra | A. Veeser; C. Lovadina |