Opennings

You are welcome to send a spontaneous application for a research position. In order to maximize your chances that we will consider it please provide a motivation letter in which you explain why you would want to work with us as well as a short research plan in which you will describe the work you want to do and how it relates to ours.

  • PhD student position, University of Geneva (Computer Science) and University of Applied Sciences (Open 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 various forms such as equation-based models (e.g. physics and chemistry), simulators (e.g. biomechanical models), and more general black-box programming artifacts (chemoinformatics RDKit). Eventually such models should be considerably more data efficient and offer additional advantages in terms of interpretability. The successful candidate will enroll as a PhD student in the Computer Science department of the University of Geneva (under the co-direction of Prof. Alexandros Kalousis and Prof. Stephane Marchand-Maillet) and, at the same time, will become a member of the Data Mining and Machine Learning group (http://dmml.ch) at the University of Applied Sciences, Geneva. The position shall be filled in as soon as possible. We seek strongly motivated candidates prepared to dedicate to high quality research. The candidate should have (or be close to obtaining) a Master's degree or equivalent in computer science, statistics, applied mathematics, electrical engineering or other related field with strong background in machine learning and programming (Pytorch and/or Tensorflow). If interested, please send the following to alexandros.kalousis@hesge.ch- academic CV (max 2 pages)- academic transcripts of BSc and MSc- one page motivation letter explaining why the candidate is suitable for the position- 500 word research proposal on one of the topics described above- contact details of three referees (do not send reference letters) Deadline for applications: 31/12/2020. In case of any further questions, please contact alexandros.kalousis@hesge.ch.

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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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  • 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.

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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.

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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.

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