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.

  • Post-doc position on generative models for inverse problems and simulation-based inference (currently open).

    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 prior scientific knowledge available in the form of simulators. We will follow the simulation-based inference paradighm to model the posterior distribution of the simulators parameters that could have generated the observed data. Of partricular interest are the settings in which the domain knowledge is incomplete and/or imperfect. Classical approaches to simulation-based inference, e.g. Approximate Bayesian Computation, suffer from high complexity costs in particular in high dimensional spaces and machine learning has great potential to address such problems . We will explore ways to explicit encode, and complete when necessary, available domain knowledge, resulting to what is known as grey-box models. Such models respect by construction the domain knowledge, will not produce implausible outputs, and have strong extrapolation performance. The position is funded by a Swiss National Science Foundation interedisciplinaty project, with funding secured for four years. The project brings together teams from geology, seismology and machine learning with the overall objective to streamline the passive seismic exploration by developing new, machine-learning based, analysis tools in order to identify potential targets for geothermal exploitation. Within this context the DMML team will develop generative models to invert the sheer wave velocities measured in earth's surface in order to recover velocity models of the subsurface. We seek strongly motivated candidates dedicated to high quality research. Candidates should have (or be close to obtaining) a Phd in machine learning, ideally in the area of generative modelling, and a strong research track-record attested by high quality publications in relevant machine learning venues such as ICML, NeurIPS, ICLR, AI-STATS, UAI, etc. The selected candidate is expected to demonstrate a high degree of independence and autonomy, drive ...

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  • PhD position on generative models for inverse problems and simulation-based inference (currently open).

    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 scientific knowledge available in the form of simulators. We will follow the simulation-based inference paradighm to model the posterior distribution of the simulators parameters that could have generated the observed data. Of partricular interest are the settings in which the domain knowledge is incomplete and or imperfect Classical approaches to simulation-based inference, e.g. Approximate Bayesian Computation, suffer from high complexity costs in particular in high dimensional spaces and machine learning has great potential to address such problems . We will explore ways to explicit encode, and complete when necessary, available domain knowledge, resulting to what is known as grey-box models. Such models respect by construction the domain knowledge, will not produce implausible outputs, and have strong extrapolation performance. The successful candidate will enroll as a PhD student in the Computer Science department of the University of Geneva (under the co-direction of myself 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. Starting date: autumn 2022. The position is funded by a Swiss National Science Foundation interedisciplinaty project, with funding secured for four years. The project brings together teams from geology, seismology and machine learning with the overall objective to streamline the passive seismic exploration by developing new, machine-learning based, analysis tools in order to identify potential targets for geothermal exploitation. Within this context the DMML team will develop generative models to invert the sheer wave velocities measured in earth's surface in order to recover velocity models of the subsurface. We seek strongly motivated candidates prepared to dedicate to high quality research. The ...

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  • Post-doc position on generative models for discrete data structures (currently open)

    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 as well as what can be considered the equivalent of style transfer for discrete structures. We expect to focus on generative models with latent variables since these allow us to actively manipulated the learning instances as well as to explicitate the available domain knowledge. Of particular interest are discrete latent spaces and the development of generative models that work natively over discrete data structures and such discrete latent spaces. The project is funded from a grant of the Swiss National Science Foundation with funding secured for up to four years. We seek strongly motivated candidates dedicated to high quality research. Candidates should have (or be close to obtaining) a Phd in machine learning, ideally in the area of generative modelling, and a strong research track-record attested by high quality publications in relevant machine learning venues such as ICML, NeurIPS, ICLR, AI-STATS, UAI, etc. The selected candidate is expected to demonstrate a high degree of independence and autonomy, drive their own research and actively contribute to the scientific development of the group through their knowledge and expertise as well as by proposing and contributing to group activities such as readings, schools, workshops etc. They are expected to participate in the supervision of a PhD student that will also be working within the same project. There is also a possibility to participate to teaching activities. The DMML group consists of around ten researchers at the PhD and Post-doc level, working in different areas of machine learning, such as generative models, imitation learning. The team collaborates closely with the VIPER group, https://viper.unige.ch, from the computer science department of the University of Geneva headed by Prof. Stephane Marchand-Maillet. In addition the group is involved in a number of national and international research projects. We offer ample opportunities ...

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  • PhD position on generative models for discrete data structures (currently open)

    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 well as what can be considered the equivalent of style transfer for discrete structures. The successful candidate will enrol as a PhD student in the Computer Science department of the University of Geneva (under the co-direction of myself 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. Starting date 1/September/2022 (negotiable). The position is funded by a Swiss National Science Foundation grant with funding secured for four years. 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 very good 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 - contact details of three referees (do not send reference letters) We will be accepting applications until the position is assigned. For any questions please contact Alexandros.Kalousis@hesge.ch

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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 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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