[Statlist] Multiple PhD positions on machine learning with simulation and physics modeling of the world

[Alexandros Kalousis]

We have several PhD openings in machine learning research for exploring 
methods to combine learning with process-driven modeling and simulations.

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) as a research and teaching assistant at HES-SO, Geneva. 
The positions shall be filled in as soon as possible.

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.

Related literature:
- Battaglia, Peter, et al. "Interaction networks for learning about 
objects, relations and physics." Advances in neural information 
processing systems. 2016.
- Lionel Blondé, Alexandros Kalousis "Sample-Efficient Imitation 
Learning via Generative Adversarial Nets." AISTATS 2019: 3138-3148
- Narayanaswamy, Siddharth, et al. "Learning disentangled 
representations with semi-supervised deep generative models." /Advances 
in Neural Information Processing Systems/. 2017.

We seek strongly motivated candidates prepared to dedicate to high 
quality research in the above domains for a number of years (the 
expected time to PhD graduation is 4-5 years). 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 as many as possible (but 
at least some) of these: machine learning, probability and statistical 
modeling, mathematical optimization, programming and software 
development (preferably Pytorch and/or Tensorflow).

If interested, please send the following to alexandros.kalousis at hesge.ch
- academic CV (max 2 pages)
- academic transcript of the study results
- 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)

The applications will be processed as they come as of now until the 
positions are filled. The status of the openings will be update here: 
http://dmml.ch/recruitment/

In case of any further questions, please contact 
alexandros.kalousis at hesge.ch. I will also be in NeurIPS/Vancouver so 
ping me if you are around.




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