Ph.D. Scholarship in Machine Learning

*** Project title: Large-scale Probabilistic Nonparametric Modeling

*** Project description:

We are looking for a Ph.D. candidate to work on the development of
novel advanced statistical methodologies in the area of probabilistic
nonparametric modeling, with applications in life sciences.

The main focus of the project is to develop novel inference and
computational methods to accurately and tractably quantify uncertainty
in probabilistic nonparametric models. In particular, the project will
target the application of probabilistic kernel machines, such as
Gaussian processes and their “deep” extension, to large-scale data
modeling problems. The successful candidate will develop and apply a
range of techniques in Markov chain Monte Carlo inference, stochastic
variational inference, linear algebra, and their parallel and
distributed versions. A large portion of the project will be dedicated
to the development of the methodology, but the project will also
contain an exciting component of applied work in life sciences, and in
particular in neuroscience.

The project is linked to a Chair in Computational Statistics entitled
“Novel Computational Approaches to Risk Modeling” awarded to Dr
Filippone from the AXA Research Fund for the duration of 7 years
(2016-2023). The successful candidate will participate in the research
activities in machine learning in the Department of Data Science at
EURECOM, and will engage in a number of exciting ongoing international
collaborations.

The successful candidate will be enrolled in the doctoral school of
the Pierre and Marie Curie University (UPMC), Paris, France, that will
award the final Ph.D. degree.

*** Main tasks and responsibilities include:
– Discuss, plan, and perform research in a stimulating environment
– Develop statistical approaches for data analysis from fundamental
principles
– Publish findings in peer-reviewed journals and present at
international conferences
– Produce software tools to enable for the use of the wider scientific
community
– Finalize Ph.D. training and project within the three years of the
Ph.D.
– Work in an interdisciplinary team of international scientists

*** Essential and desirable requirements include:
– Completion of a degree in Computer Science, Statistics, Physics,
Mathematics, Neuroscience or related disciplines
– Proficiency in programming in languages such as Python, MATLAB, R or
C++
– Good written and oral communication skills, and effective team-work
skills
– We are looking for highly self-motivated candidates who are curious
and enthusiastic about scientific research, and have a proactive
attitude
– Experience with life science applications and track record of
publication are desirable

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*** EURECOM and the Department of Data Science

EURECOM is a French graduate school and a research center based in the
international science park of Sophia Antipolis (French Riviera), which
brings together renowned universities such as Télécom ParisTech, Aalto
University, Politecnico di Torino, Technische Universität München
(TUM), Norwegian University of Science and Technology (NTNU), Chalmers
University (Sweden) and Czech Technical University in Prague
(CTU). The Principality of Monaco is a new institutional member. The
Institut Mines-Télécom is EURECOM’s founding member. EURECOM benefits
from a strong interaction with industry through its specific
administrative structure: Economic Interest Group, which brings
together international companies such as: Orange, ST Microelectronics,
BMW Group Research & Technology, Symantec, Monaco Telecom, SAP,
IABG. EURECOM deploys its expertise around three major fields: Data
Science, Digital Security, and Communication Systems.

The Department of Data Science was created at the beginning of 2016 to
establish and consolidate a number of new activities at EURECOM in
Data Science and Engineering. Its main research topics include:
Machine learning, statistical modeling, deep learning, large-scale
data mining and fusion, information extraction and knowledge base
population, game theory, adversarial learning and economics models of
Data, distributed systems and data management systems. The department
hosts its own data center and computing platform featuring 1000+
computing cores, 2.5 TB of RAM, and several hundreds TB of storage.