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.
Human locomotion is a highly complex dynamical system and learning locomotion models is a challenging task. Pathological locomotion modelling is even more challenging due to the limited availability of training data. This project will address these challenges by bringing together machine learning and neuromechanical simulation. On the machine learning side we will explore a number of research problems such as learning of dynamical systems using conditional generative models, learning with simulated data, learning, tunning and improving simulators. A simulator is a model of domain knowledge and as such it can be imprecise and incomplete. We are particularly interested in the tight integration of simulation and learning and their interactin in a virtuous cycle with each one improving the other; relevant work includes interaction networks and the neural physics engine.
The position is funded by SNSF for four years.
The successful candidate will join the Data Mining and Machine Learning group at the Department of Information Systems of University of Applied Sciences, Western Switzerland, led by Prof. Alexandros Kalousis, and will enroll as a PhD student at the Computer Science department of the University of Geneva within the VIPER group led by Prof. Stephane Marchand-Maillet. Our research explores a number of different issues such as: learning in high dimensional settings, dimensionality reduction and feature selection, learning with structured data (multiple kernel learning), metric and similarity learning, the exploitation of domain knowledge in the learning process. For a more detailed description the interested candidates may take a look at our website.. The greater Geneva lake area is a world-renowned education and research hub, including not only the University of Geneva, but also EPFL, and IDIAP. It offers considerable opportunities for training and exposure to data mining and machine learning, with a number of research teams being active on these and related fields. In addition the selected candidate will have ample opportunities to participate in the main ML and DM conferences.
The ideal candidate will have:
- A very solid background in a combination of mathematics and computer science. Special areas of interest include: statistical machine learning, statistics, mathematical optimization, mathematical modelling.
- He or she should have completed, or about to complete, an MSc in the above areas.
- A very good understanding of machine learning methods and algorithms; project experience in the application area will be a plus.
- Solid expertise in learning frameworks such as PyTorch, TensorFlow.
- Solid programming skills in scripting languages, such as perl, python, etc.
- Excellent command of English.
- Team work capacity.
Candidates should send:
- A two page CV.
- A one page motivation letter explaining why their skills, knowledge and experience make them a particularly suitable candidate for the given position.
- The academic transcripts of their studies.
- A 500 words research proposal on the the learning problematic described above.
- The contact details of three referees; do not send reference letters.
Applications will be processed, starting from 1/June/2019, as they arrive and accepted until the position is filled. The status of the position will be indicated here.
The position will be available ASAP.