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Category Archives: recruitment
Post-doc position on grey-box machine learning (currently open)
The Data Mining and Machine Learning group (http://dmml.ch/) at the University of Applied Sciences in Geneva has an opening for a full-time post-doc position. The research target is to develop grey-box (hybrid) machine learning methods that combine data-driven models such … Continue reading
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Tagged Alexandros Kalousis, dmm, machine learning, Naoy Takeishi, postdoc, project, recruitment, research project, snsf
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Post-doc position on generative models for inverse problems and simulation-based inference (Closed position).
We have an opening for a full-time post-doc position. The research target is to develop machine learning methods to solve inverse problems accounting for the inherent uncertainty of the inverse problem. We will formulate these problems as inference problems with … Continue reading
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PhD position on generative models for inverse problems and simulation-based inference (Closed position).
We have an opening for a PhD position. The research target is to develop machine learning methods to solve inverse problems accounting for the inherent uncertainty of the inverse problem. We will formulate these problems as inference problems with prior … Continue reading
Posted in news, recruitment
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Post-doc position on generative models for discrete data structures (Closed position)
We have an opening for a full-time post-doc position on a research project on the development of generative models for discrete data structures, such as graphs and in particular molecules. We seek to develop generative models capable of conditional generation … Continue reading
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PhD position on generative models for discrete data structures (Closed position)
We have an opening for a PhD position. The research target is the development of deep generative models for discrete data structures such as graphs and in particular molecules. We seek to develop generative models capable of conditional generation as … Continue reading
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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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Tagged deep leaning, generative modeling, job openings, machine learning, phd, phd in machine learning, phd student
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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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Tagged deep leaning, job openings, machine learning, phd, phd in machine learning, phd student, recruitment
Comments Off on Multiple PhD positions in machine learning with simulation and physics modeling of the world (closed)
PhD position on learning and simulation for human locomotion modelling (closed)
We have an opening for a PhD position on the development of machine learning methods for the modelling of pathological human locomotion in the framework of a collaborative Sinergia project funded by the Swiss National Science Foundation. The goal of the project is to develop, through machine learning and neuromechanical simulation, accurate models of human locomotion, together with Stephane Armand (University of Geneva, Kinesiology laboratory) and Auke Ijspeert (Biorobotics laboratory, EPFL, BIOROB). The project will (1) model pathological gaits resulting from motor impairments such as cerebral palsy, and (2) compare and combine neuromechanical simulation and machine learning approaches for gait analysis. It brings together expertise on pathological gait, neuromechanical simulation models, machine learning, coupled with a unique collection of relevant real world data. Continue reading
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PostDoc position on learning and simulation for human locomotion modelling (closed)
We have an opening for a PostDoc position on the development of machine learning methods for the modelling of pathological human locomotion in the framework of a collaborative Sinergia project funded by the Swiss National Science Foundation. The goal of the project is to develop, through machine learning and neuromechanical simulation, accurate models of human locomotion, together with Stephane Armand (University of Geneva, Kinesiology laboratory) and Auke Ijspeert (Biorobotics laboratory, EPFL, BIOROB). The project will (1) model pathological gaits resulting from motor impairments such as cerebral palsy, and (2) compare and combine neuromechanical simulation and machine learning approaches for gait analysis. It brings together expertise on pathological gait, neuromechanical simulation models, machine learning, coupled with a unique collection of relevant real world data. Continue reading
Posted in recruitment
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Postdoc position on generative modelling for complex objects (closed)
We are looking for an excellent postdoc to work on the development of deep learning methods for the automatic composition of complex structures, such as sets, graphs, trees, sequences, that exhibit desired properties. Typical application scenarios include image and text generation, drug and molecule design. Research areas of direct interest include generative modeling, unsupervised and semi-supervised learning. Continue reading
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