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 position on human musculo-sceletal modelling for pathological gait modelling, Metathesis project (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 phd position funded by a Swiss National Science Foundation, funding is available for up to four years. Within the project we seek to develop learning methods and models that will accurately replicate human locomotion from observations only, i.e. sequences of state-state transitions as these are captured by motion capture systems. The overall objective of the project is the modelling of pathological human gait in order to be able to explore the result of different theurapetic approaches. The project is a collaborative effort with the Kinesiology Lab of the University of Geneva . Within the project we will follow two approaches to model gait. A generative modelling approach in which we will seek to exploit the presence of locomotion simulators, potentially differentiable, in order to learn to generate gait that is compliant by construction with the underlying physics of motion, see our previous work . Additional challenges that we want to address include the high dimensionality of the state and action spaces. 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 at the University of applied sciences, Geneva. 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, applied mathematics, electrical engineering or other related field with very strong background in machine learning programming environments (Pytorch, ...
Read morePost-doc position on human musculo-sceletal modelling for pathological gait modelling, Metathesis project (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 funded by a Swiss National Science Foundation, funding is available for up to four years. Within the project we seek to develop learning methods and models that will accurately replicate human locomotion from observations only, i.e. sequences of state-state transitions as these are captured by motion capture systems. The overall objective of the project is the modelling of pathological human gait in order to be able to explore the result of different theurapetic approaches. The project is a collaborative effort with the Kinesiology Lab of the University of Geneva . Within the project we will follow two approaches to model gait. A generative modelling approach in which we will seek to exploit the presence of locomotion simulators, potentially differentiable, in order to learn to generate gait that is compliant by construction with the underlying physics, see our previous work . Additional challenges that we want to address include the high dimensionality of the state and action spaces. 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, ...
Read morePost-doc position on generative models for inverse problems and simulation-based inference, Migrate project (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 funded by a Swiss National Science Foundation interdisciplinary project. Within the project we develop models, namely generative ones, for inverse problems following simulation-based inference approaches . We 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. Funding is secured for up to three 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 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 PhD students. The DMML group consists of roughly a dozen of researchers at the PhD and Post-doc level, working in different areas of machine learning, such as generative models, imitation ...
Read morePost-doc position on distributed multi-agent reinforcement learning, Hyper AI project (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 funded by the Swiss confederation in the context of an Horizon Europe collaborative project. Within the project the DMML group will develop distributed multi-agent reinforcement learning methods for collaborative decision making , i.e. with whom and when they should exchange information. The developed methods will be eventually deployed for distributed resource management on network continuum (IoT, Edge, Cloud). 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 PhD students. The DMML group consists of roughly a dozen of researchers at the PhD and Post-doc level, working in different areas of machine learning, such as generative models, reinforcement and 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. We offer ample opportunities and support for scientific development, e.g. providing funding for conferences, schools, research visits and exchanges etc. We strive to provide a research environment in which researchers can focus on their research and allow for space and time to develop solid ideas. The group regularly publishes to some of the best ...
Read morePost-doc position on grey-box machine learning (closed)
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 as deep neural nets and theory-driven, physical and/or causal models .The position is funded by a 3-year project of the Swiss National Science Foundation (SNSF) in the frame of the Strategic Japanese-Swiss Science and Technology Programme (SJSSTP). The project is for developing interpretable condition monitoring methods for complex engineering systems. The aforementioned grey-box machine learning methods will be utilized as building blocks of a condition monitoring (e.g., anomaly detection) framework that should hold a certain extent of interpretability. The research will be conducted in close collaboration between the DMML group and a Japanese counterpart, the Artificial Intelligence Lab at RCAST, the University of Tokyo (https://sites.google.com/g.ecc.u-tokyo.ac.jp/ailab/top-english), who will mainly work on the implementation of a condition monitoring framework and its deployment on real-world systems.We seek strongly motivated candidates dedicated to high-quality research. Candidates should have (or be close to obtaining) a PhD in machine learning or related areas and a strong research track record attested by high-quality publications in relevant machine learning venues such as ICML, NeurIPS, ICLR, AISTATS, UAI, etc. The selected candidate is expected to demonstrate a high ...
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