PositionsLast update: 3, May 2016
This page is devoted to the publication of available positions for research jobs (assist. Prof., Post doc, PhD) within the fields of interest of the TC-15. If you wish to publish an announce on this page please send an email to the tc15 chairman with the following information:
- One short title
- The date of expiration of the announce
- the coordinates of a contact for the position (with name, and emails)
- A description of the position
- an eventual link to a web page
GraphScore - Definition and evaluation of graph scores in complex interaction networks (post-doctoral position, one year renewable, starting before march 31st 2018)
- Complex interaction networks, Labeled property graph model, scoring methods, prioritization.
- Context :
Work at the interface between the Centre Hospitalier Regional Universitaire (CHRU) of Nancy and University of Lorraine represented by the computer science laboratory LORIA (http://www.loria.fr), in connection with a hospitalo-universitary research project about Heart Failure (workpackage dedicated to complex networks analysis) and the development at the LORIA of a shared ressource platform for « data science for healthcare ».
- Job Description:
The objective of the project is to define scoring methods applicable to graphs in order to compare them and prioritize the most relevant ones.
The main ressource at our disposal is a huge graph database representing various types of interactions between various groups of elements : proteins, diseases, drugs, etc. Queries on the main graph database return several subgraphs that need to be ranked according to given priorities. Several graph scoring methods will be defined, combining graph topological properties and any other properties attached to the graph nodes and edges, these latter properties being expressed in controlled vocabularies or ontologies.
The scoring methods will be implemented and tested through evaluation studies, based on benchmark datasets.
The main application of the project is to identify new biomarkers of given heart-failure mechanisms.
- PhD thesis in Computer Science or Applied Mathematics dealing with complex graph analysis or mining.
- Computer Science : relational database (ex : MySQL), graph-oriented databases (ex : Neo4J), knowledge bases, safety of information systems, programming languages (bash, python, R, php, java, others…), knowledge in statistics and in supervised or unsupervised classification/machine learning.
- Some experience in working in an inter-disciplinary environment related to health or biology, information retrieval and/or high-performance computing will be appreciated.
- Gross monthly salary: 2500 to 3000 euros depending on experience and qualifications.
- Health insurance is included in the gross salary.
- 45 days vacation per year
- There will be substantial financial support for conference travel and international outreach.
- Life in Nancy (France) is relatively cheap with a one-person flat available starting from 500 euros.
- Application details:
The candidate should send by e-mail:
- a motivation letter,
- a detailed CV
- the PhD thesis abstract, date of obtention and jury composition, the coordinates (e-mail and tel.) of one or two reference persons
PhD position Tours (France) Sept 2018: Adaptive and budgeted Graph Mining with Evolutionary algorithms
Nicolas MONMARCHE, Jean-Yves RAMEL
LIFAT – Laboratoire d’Informatique Fondamentale et Appliquée de Tours (EA6300) - Université de Tours - France
- Graph mining, graph matching, machine learning, pattern recognition, Ant colony, métaheuristics
Graphs can easily describe complex entities or objects. They have the important benefits of unconstrained dimensionality, substructure emergence and interpretability, as opposed to numerical data classically involved in machine learning. These properties are of particular interest for many applications including chemoinformatics, bioinformatics, computer vision, video indexing, text retrieval, social network and Web analysis. However, despite a mature graph theory, graph-based Machine Learning approaches are lagging behind compared to statistical machine learning. Bottlenecks regarding graph learning and error-tolerant graph matching are both theoretical, methodological, and connected with implementation issues since manipulating graphs is known to be computationally very demanding. In this context, the scientific purpose of this collaborative research program is to create a significant breakthrough on three bottlenecks linked with the combination of graph representations and matching (GM) and machine learning (ML).
After a study of the recent work done in the 2 domains (algorithms for measuring similarity between graphs on the one hand, and methods of colony-based optimization on the other hand), the following main questions, connected to these hot topics of the moment, will be addressed during the PhD:
- how to combine machine learning methods (Active Learning) and optimization (artificial ants) to produce an adaptive method of similarity computation between graphs and graph-prototype generation;
- how to optimize these methods by creating "budgeted" or "Anytime" versions in order to make it usable online (in constrained time) on real data (scalability);
- the evaluation of the proposed methods in the context of real-life applications dealing with structural data (social network analysis, 3D object comparison, bio-informatics, ...)
- 3 years grant from the French Ministry of Higher Education and Research. Health insurance is included in the gross salary. Life in Tours (France) is relatively cheap with a one-person flat available starting from 500 euros.
- Application details:
- The candidate should send by e-mail: a motivation letter, a detailed CV the Master thesis abstract, the coordinates (e-mail and tel.) of one or two reference persons.
- jean-yves.ramel [at] univ-tours.fr – nicolas.monmarche [at] univ-tours.fr
PhD+PostDoc positions @ PRIP, TU Wien, Austria
- The Pattern Recognition and Image Processing Group (PRIP) of the Institute for Computer Graphics and Algorithms at TU Wien invites applications for an assistant position for a doctoral candidate (Univ.Ass. PostDoc). The position is for the duration 5/2018-6/2020 - at the longest till end of maternity leave and is paid (at least) € 50.772,40 per year at 40 hours per week.
- The Pattern Recognition and Image Processing Group (PRIP) of the Institute for Computer Graphics and Algorithms at TU Wien invites applications for an assistant position for a PhD candidate (Univ.Ass. PreDoc). The position is for a duration of four years and is paid according to pay scale B1 at 25 hours per week.
- job (both positions):
- teaching bachelor and master courses;
- basic and applied research in pattern recognition and image processing with the focus on hierarchical and structural methods and representations;
- publishing results at high-level workshops, conferences and journals.
- official announcement:
- on this web page
Description of the position
Graph kernels have already been applied to chemoinformatics and are based on structural information encoded within molecular graphs. However, intrinsic properties of atoms and theirs interactions induce some electronic properties which are not explicitly encoded within classic molecular graphs representations. The main purpose of this post doctoral position is to include this information into a new augmented kernel and apply it on some chemoinformatics datasets. The two main steps will be i) to define a new molecular representation encoding local electronic information and ii) to define a new similarity measure as a kernel to compare two molecules encoded in the new proposed representation.
This project will be supervised in close collaboration by LITIS (Rouen, France) and GREYC (Caen, France) laboratories which have a strong expertise on graph kernels for chemoinformatics. The chemical part will be supervised by COBRA laboratory (Rouen, France) which has proposed various atomic descriptors encoding some electronical information. Their expertise will be essential to be able to encode additional information into a new representation for chemical compounds.
Salary:This position will be granted with about 2280 euros/month net salary.
Application domains:machine learning on graphs, chemoinformatics, graph kernels, graph representations
- Place: The research will be conducted at LITIS Laboratory (Rouen, France) in Normandy. The LITIS (EA 4108) is affiliated to Normandie University, University of Rouen and INSA Rouen Normandie.
- Start date: January/ February 2018
- Duration: 20 months according to discussions with the candidate.
- Topics: Graph kernels, graph representation, machine learning
- Contact: You can contact the team via : firstname.lastname@example.org
- Required skills:
- PhD or Master in Applied Mathematics or computer science,
- experience in C++, Python or Matlab programming,
- knowledge in kernel methods, graph based approach constitutes an advantage.
- Required documents: Please send the following documents:
- up to date CV,
- Any recommendation letter
- A short document on research experience and interests