TraDE-Opt’s ESRs will have a unique opportunity to excel in the academic and nonacademic career. TraDE-Opt’s training of the involved ESRs will be personalized and learning centric, and traditional formats will be complemented with a “learning by doing” approach. The adopted methodology has the goal of developing and improving each one’s skills, knowledge, and interests, as a means of maximizing each one’s potential and career prospects.

ESR1. Fast iterative regularization through dynamical systems

Host Institution:UniGe

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ESR2. Exploiting geometry in optimization for data science

Host Institution: UniGe

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ESR3. Efficient convex relaxations of non-convex problems arising in signal processing and computer vision

Host Institution: CS

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ESR4. Mouna Gharbi

Accelerated unfolded MM approaches

Host Institution: CS

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ESR5.Incremental methods for huge scale image reconstruction

Host Institution: TUBS

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ESR6.Flexible hierarchical splitting of convex optimization problems

Host Institution: TUBS

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ESR7. Non-stationary preconditioning and multiscale approaches for variational imaging

Host Institution: U-GRAZ

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ESR8.Regularized algebraic reconstruction techniques

Host Institution: U-GRAZ

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ESR9.Scalable optimization algorithms for huge-scale optimization problems

Host Institution: UPB

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ESR10. Significant advances in the state of the art of decision-making for complex network systems

Host Institution: UPB

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ESR11. Giovanni Bruccola

Global optimization tools for big data problems

Host Institution: SRI PAS

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ESR12. Hung Tran

Projection methods in convex splitting algorithms for huge data problems

Host Institution: SRI PAS

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ESR13. Performance estimation and design of optimal optimization methods in data science

Host Institution: UCL

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ESR14. Automated method selection and tuning for optimization problems in data science

Host Institution: UCL

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ESR15. Unsupervised features learning from multivariate time series

Host Institution: CAMELOT

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“Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less. -Marie SKŁODOWSKA-CURIE”