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Field
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Partial Differential Equations, Dynamical Systems, Optimization, Scientific Computing, Complex Systems, and Network Science. Spanish language skills are not required. Our working language is English. Both
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, with the possibility of renewal, in the field of Analysis and Partial Differential Equations (PDEs). Job description - The successful candidate will collaborate on research projects related
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the below: Indicative Syllabus Laplace transforms: the Laplace transform of elementary functions, properties of the Laplace transform, inversion of the Laplace transform, solving differential equations using
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Laplace transforms: the Laplace transform of elementary functions, properties of the Laplace transform, inversion of the Laplace transform, solving differential equations using Laplace transforms and the
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also maintains strong research activity in partial and ordinary differential equations, dynamical systems, and systems and control theory, alongside active work in mathematical physics, including quantum
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of special functions, and appear naturally in physics, but also, for example, in combinatorics (finite differential generating series) or in algebraic geometry (Picard‑Fuchs equations). A particularly
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on probability, partial differential equations, large deviations, stochastic analysis, optimisation and machine learning. The successful candidate will contribute to the activities of the Machine Learning & Data
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conduct research on numerical methods for partial differential equations, with a particular focus on applications in engineering, physics, life sciences, and earth sciences. The research will focus
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methods for differential equations and scientific machine learning; geometric deep learning, manifold learning, equivariant methods, high-dimensional geometry, and structure-aware learning; and the
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. Candidates whose work connects rigorous mathematics with modern computation and complements departmental strengths in topology, algebraic topology, analysis, partial differential equations, geometric analysis