Tag Archives: phd in machine learning

PhD Thesis defense of Amina Mollaysa

Amina Mollaysa will defend her PhD Thesis entitled "Structural and Functional Regularization of Deep Learning Models" on Friday 26/02/2021, at 15h00 CET. If you are interested in attending the PhD defence of Amina Mollaysa, please send an email with the title "Thesis defense Mollaysa - Zoom" to alexandros.kalousis@hesge.ch to receive the Zoom link.

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PhD student position, University of Geneva (Computer Science) and University of Applied Sciences (Closed position)

We have an opening for a PhD position. The research target is the development of deep generative models that can incorporate strong domain knowledge within the learning process. Such domain knowledge, typically available in scientific fields, can be encoded in … Continue reading

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Multiple PhD positions in machine learning with simulation and physics modeling of the world (closed)

We have several PhD openings in machine learning research for exploring methods to combine learning with process-driven modeling and simulations. The interaction and cooperation between a simulator and a machine learning model can be exploited in a number of areas where data are expensive or difficult to obtain, and/or where domain knowledge within the process-driven models can back the inductive biases factored into the machine learning models. In the medical domain, machine learning methods can be combined with neuromechanical simulators to develop models of human locomotion that shall support critical medical decisions related to surgical interventions treating pathological gait patterns. In industrial manufacturing, simulations and physical modeling of realistic or extreme operational conditions can support the learning of rare faulty behaviours in order to trigger early alerts. In chemoinformatics, an external system (e.g. RDKit) can provide relevant constraints for generating valid new molecules with specific required characteristics.

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