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instead use live microscopy and automated image analysis to individually study the behaviour of thousands of cells. This single cell approach will allow us to resolve key checkpoints in the germination
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analysis and/or advanced algebra or algebraic topology. Knowledge and experience of machine learning. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work
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applications in mathematics (combinatorics, algebras, numerical analysis, stochastic analysis…) and physics (renormalisation theory, string theory…). Recently, some new structures on rooted trees have appear in
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, United States of America [map ] Subject Areas: Mathematics, Probability and Statistics, Financial Mathematics, Actuarial Mathematics, Stochastic Analysis, Data Science, and Statistical Learning Appl Deadline: 2026/06/01 04
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methods, Mathematical optimisation techniques, Physics-informed neural networks, Scientific programming in Python, MATLAB or equivalent, Analysis of complex multi-modal biomedical datasets Skills Essential
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, we believe science can achieve its fullest potential. THE ROLE During your internship you will work on a projectin the Computational mOdelling of NanosCalE and bioPhysical sysTems (https
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of Mathematics, Computer Science and Statistics at the Faculty of Sciences is a leading academic unit. It consists of a group of highly qualified lecturers, researchers, and administrative staff. They work closely
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI SCIENZE STATISTICHE "PAOLO FORTUNATI" | Italy | 11 days ago
14 Mar 2026 Job Information Organisation/Company ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI SCIENZE STATISTICHE "PAOLO FORTUNATI" Research Field Computer science Mathematics
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, or Number Theory. The Department offers undergraduate and graduate programs in mathematics, applied mathematics, and statistics, and plays a key role in interdisciplinary research across the College
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance