This position has been filled.
A 3-years PhD position is available at the SAMM laboratory
of the Sorbonne university, in Paris. We are looking for a dynamic
candidate with a master degree related to machine learning. Candidates
with a strong statistical background must be fluent in R (knowledge of
C/C++/Java will be appreciated). Candidates with computer science
backgrounds must be fluent in C, C++ or Java. The candidate does not
need to speak French if he/she is fluent in English. The student will be
supervised by myself, in collaboration with assistant
professors of the statistical learning and networks research axis
(mainly Nicolas Bourgeois, Pierre Latouche, Madalina Olteanu and
- after payment of health insurance, taxes, and pension, ranges from
1450€ to 1700€ depending on whether the candidate decides to give lab
sessions for students
- starting date
- as soon as possible.
- application documents
- CV with references, grades obtained in the master,
anything else than can demonstrate the capabilities of the applicant
(master thesis for instance). Everything must be in
pdf only. Applications transmitted in opaque file formats (such as
word) will not be considered.
Subject: Graph data analysis with application to social sciences and history
Because graphs are simple data structures yet capable of
representing complex systems, they are used in numerous scientific
fields from computer science to social sciences. For instance, in
biology, metabolic networks focus on representing pathways of
biochemical reactions while the regulation of genes through
transcriptional factors is described using regulatory networks.
Recently, there has been a growing interest in studying historical
networks which are more and more easily available via digitization of
various sources. Protohistorical networks can be inferred using material
traces: for instance amphorae tracking is used to derive commercial
exchanges between Roman or Greek antique cities, leading to a graph of
those cities, decorated by the type of exchanges between them. When
written sources are available, finer grained networks can be inferred,
especially between persons with the so called prosopographic method.
Examples of such networks studied in the SAMM laboratory include ties
between ecclesiastics and nobles in the Merovingian Gaul, feudal
contracts between peasants and nobles in the middle ages, exchanges
between Celtic cities in the fourth century BC, etc.
Those networks lead to numerous research questions including:
- how to compare different networks on a global point of view?
- how to identify local differences/resemblances between different
- how to infer missing decoration on vertices and/or edges of networks?
- how to qualify/quantify network evolution through time?
- how to compare the evolution through time of different networks?
To address those questions, we use a large body of methods ranging from
exact combinatorial algorithms to random graph models, and including non
metric data mining methods. The methodological choices of the PhD will
be adapted to the candidate, taking into account his/her background.
Selected SAMM articles on historical data
- Batch kernel SOM and
related Laplacian methods for social network analysis.
R. Boulet, B. Jouve, F. Rossi et N. Villa. Neurocomputing,
volume 71, numéro 7-9, pages 1257-1273, Mars 2008.
- Exploration of a Large Database of French Charters with Social Network Methods.
F. Rossi, N. Villa-Vialaneix et F. Hautefeuille. Dans
International Medieval Congress (IMC 2011), Leeds (United Kingdom),
- Hidden Markov models for time series of counts with excess zeros.
Olteanu M., Ridgway J. Dans Proceedings of ESANN 2012 - European
Symposium on Artificial Neural Networks, Belgique (2012)
- Lexical recount between Factor Analysis and Kohonen Map: mathematical
vocabulary of arithmetic in the vernacular language of the late Middle
Ages, N. Bourgeois, M. Cottrel, B. Deruelle, S. Lamassé, P. Letrémy,
WSOM 2012, AISC 198, pp. 255-264, 2012.
- The random subgraph model for the analysis of an ecclesiastical network in Merovingian Gaul.
Y. Jernite, P. Latouche, C. Bouveyron, P. Rivera,
L. Jegou, S. Lamassé, Arxiv, (2012)
- Cartographie de la Chronique d'Henri de Livonie, N. Bourgeois, Revue des
nouvelles technologies de l'information, Janvier 2013.
- Spatial correlation in bipartite networks.
N. Villa-Vialaneix, B. Jouve, F. Rossi et F. Hautefeuille. Revue des nouvelles technologies de
l'information, pages 97-110, Janvier 2013.
- Activity Date Estimation in Timestamped Interaction Networks
F. Rossi et P. Latouche. Dans Proceedings of the XXIth European
Symposium on Artificial Neural Networks, Computational Intelligence and
M achine Learning (ESANN 2013), pages , Bruges, Belgique, Avril 2013