The Data Mining Forum 
IMPORTANT: This is the old Data Mining forum.
I keep it online so that you can read the old messages.
Please post your new messages in the new forum: https://forum2.philippe-fournier-viger.com/index.php
Post-doc positions in data mining
Posted by:
webmasterphilfv
Date: July 08, 2012 11:33AM
I received this in my e-mail.
==========
A post-doctoral position in the area of data and text mining is open at AgroParisTech (Paris, France)
Title: "Learning to classify text when labels are taken in ontologies. Search for strategies to optimize the uncertainty of the classification"
Location: AgroParisTech, Paris, France
Research unit: UMR AgroParisTech/INRA MIA-518
Duration: 12 months starting in September 2012
Salary: around 40Keuros / year
Applications are invited for a post-doctoral position to undertake research in a project financed by the French ANR agency. This project aims at learning from texts labelled by experts how to evaluate the uncertainty associated with the assessment and management of risks related to food consumption.
The scope of the post-doctoral position is to look for ways to adapt machine learning techniques to predict the class of textual entries taken from official documents. One specific aspect of the learning task is that the classes to be predicted belong to hierarchies of concepts. In order to optimize its performance, the learning system can choose the best level in the hierarchy to make its prediction depending on the text to be labelled and the evidence available. The field of research is therefore related to multi-objective learning from semi-structured data and with text mining.
Requirements:
- Master's degree or equivalent in Computer Science, Mathematics, or Statistics
- PhD in Machine Learning, Data Mining and/or Text Mining
- Strong publication record in the fields of data mining, machine learning or computational statistics.
- Strong commitment to research and scientific publication
- Strong background in programming languages such as Python, C++, C# or Java
- Ability to produce high-quality data mining implementations, to efficiently analyze large databases
- Good oral and written English skills.
Applicants should submit the following documents, written in English or in French:
- Curriculum vitae
- Names of two references (with e-mail addresses);
- Letter of motivation
- Brief statement on how the applicant's research interests correspond with the above mentioned topics (1 - 2 pages);
- List of publications, and, if possible, link to the thesis.
Completed applications should be sent to Antoine Cornuéjols (antoine dot cornuejols at agroparistech.fr) before July 17th, 2012.
For further information, contact antoine dot cornuejols at agroparistech.fr
------------------------------------------------------------------------------------
Antoine CORNUEJOLS
Professeur d'informatique
AgroParisTech
Département MMIP
16, rue Claude Bernard, F-75 231 PARIS Cedex 05
Tél. (+33) 1 44 08 72 29 - Fax (+33) 1 44 08 16 66
Email : antoine.cornuejols@agroparistech.fr
Web : http://www.lri.fr/~antoine/
==========
A post-doctoral position in the area of data and text mining is open at AgroParisTech (Paris, France)
Title: "Learning to classify text when labels are taken in ontologies. Search for strategies to optimize the uncertainty of the classification"
Location: AgroParisTech, Paris, France
Research unit: UMR AgroParisTech/INRA MIA-518
Duration: 12 months starting in September 2012
Salary: around 40Keuros / year
Applications are invited for a post-doctoral position to undertake research in a project financed by the French ANR agency. This project aims at learning from texts labelled by experts how to evaluate the uncertainty associated with the assessment and management of risks related to food consumption.
The scope of the post-doctoral position is to look for ways to adapt machine learning techniques to predict the class of textual entries taken from official documents. One specific aspect of the learning task is that the classes to be predicted belong to hierarchies of concepts. In order to optimize its performance, the learning system can choose the best level in the hierarchy to make its prediction depending on the text to be labelled and the evidence available. The field of research is therefore related to multi-objective learning from semi-structured data and with text mining.
Requirements:
- Master's degree or equivalent in Computer Science, Mathematics, or Statistics
- PhD in Machine Learning, Data Mining and/or Text Mining
- Strong publication record in the fields of data mining, machine learning or computational statistics.
- Strong commitment to research and scientific publication
- Strong background in programming languages such as Python, C++, C# or Java
- Ability to produce high-quality data mining implementations, to efficiently analyze large databases
- Good oral and written English skills.
Applicants should submit the following documents, written in English or in French:
- Curriculum vitae
- Names of two references (with e-mail addresses);
- Letter of motivation
- Brief statement on how the applicant's research interests correspond with the above mentioned topics (1 - 2 pages);
- List of publications, and, if possible, link to the thesis.
Completed applications should be sent to Antoine Cornuéjols (antoine dot cornuejols at agroparistech.fr) before July 17th, 2012.
For further information, contact antoine dot cornuejols at agroparistech.fr
------------------------------------------------------------------------------------
Antoine CORNUEJOLS
Professeur d'informatique
AgroParisTech
Département MMIP
16, rue Claude Bernard, F-75 231 PARIS Cedex 05
Tél. (+33) 1 44 08 72 29 - Fax (+33) 1 44 08 16 66
Email : antoine.cornuejols@agroparistech.fr
Web : http://www.lri.fr/~antoine/
Re: Post-doc positions in data mining
Posted by:
webmasterphilfv
Date: July 20, 2012 09:27AM
Another post-doc position in data mining that was advertised in my emails:
==========================================
A post-doc position is about to be available at INRIA Rennes, in the frame of the 2012 INRIA national post-doc campaign.
Applications should be submitted by means of the INRIA recruitment platform: left button on the page http://tinyurl.com/c7xhus3.
INRIA project-team DREAM
INRIA Centre de Rennes, Bretagne Atlantique/IRISA, France
Duration : 12 to 16 months
Beginning : before the end of year 2012
Post-doctoral position
Mining multi-scale and multi-variate environmental data for knowledge discovery
Context
In many scentific domains, sensors are used for observing systems and for studying the evolution of these systems' behavior in time and/or space.
In the agro-environmental domain, more and more sensors are recording the manifestations and the evolution of natural phenomena.
The data, recorded as time series, are used to design or to confirm scientific theories that explain the behavior of eco-systems.
For scientists, the difficulty of data analysis rises with the size of recorded data.
They are particularly desiring tools that could facilitate the emergence of interesting features from recorded data, e.g. a relevant regularity or divergence.
Problem
Interesting phenomena emerge more or less obviously in times series with respect to the selected abstraction level: some phenomenon may appear clearly when data are abstracted week by week whereas it is difficult to observe the same phenomenon at the day or month level.
Moreover, the optimal abstraction level evolves during time with the evolution of the context in which the measures are recorded.
In addition, when several sensors record different aspects of the same phenomenon, the measures are often correlated.
These correlations should be displayed to the scientists, such as the temporal causality, e.g. "when the value of some variable rises the value of some other variable decreases with a delay of 3 to 5 days".
Objective
The goal of this project is to design new methods for extracting multi-scale and multi-variate temporal patterns coming from different sensors.
This goal rises some difficult issues. What are the main scales present in the data? What are the relationships between these scales? What are the relationships between the different variables? How to model these relationships and reason with this model?
Tasks
• analyze symbolic and numerical representation proposals for multi-scale time series,
• analyze machine learning and temporal data mining methods of multi-scale multi-variate time series,
• propose algorithms for the simultaneous extraction of multi-scale and multi-variate temporal patterns from several time series,
• implement a prototype of the proposed algorithms,
• assess the proposal on artificial data and on a real dataset provided by INRA recording water quality at the exit of a watershed. The results will be discussed with expert in the agro-environmental domain.
Profile
• PhD in computer science, preferably with a speciality in data mining or symbolic or statistical machine learning
• knowledge in time series analysis, if possible
• good programming skills
Bibliography
[1] Euzenat J., An algebraic approach to granularity in time representation, Proc. 2nd IEEE international workshop on temporal representation and reasoning (TIME), pp 147-154, 1995.
[2] Castro N., Azevedo P., Multiresolution Motif Discovery in Time Series, in Proceedings of the SIAM International Conference on Data Mining (SDM 2010), 2010, pp. 665-676.
[3] Thomas Guyet; René Quiniou. Extracting temporal patterns from interval-based sequences, in International Joint Conference on Artificial Intelligence (IJCAI), Jul 2011, Barcelone, Spain
[4] Shahar Y, Musen MA., Knowledge-based temporal abstraction in clinical domains. Artif. Intell. Med. 1996 Jul;8(3):267-98.
Keywords
machine learning ; data mining ; temporal patterns ; multi-scale ; multi-variate ; time series
Contacts
René Quiniou (INRIA/IRISA - rene.quiniou@inria.fr) Thomas Guyet (Agrocampus Ouest/IRISA - thomas.guyet@agrocampus-ouest.fr) Alice Aubert (INRA - Alice.Aubert@rennes.inra.fr)
==========================================
A post-doc position is about to be available at INRIA Rennes, in the frame of the 2012 INRIA national post-doc campaign.
Applications should be submitted by means of the INRIA recruitment platform: left button on the page http://tinyurl.com/c7xhus3.
INRIA project-team DREAM
INRIA Centre de Rennes, Bretagne Atlantique/IRISA, France
Duration : 12 to 16 months
Beginning : before the end of year 2012
Post-doctoral position
Mining multi-scale and multi-variate environmental data for knowledge discovery
Context
In many scentific domains, sensors are used for observing systems and for studying the evolution of these systems' behavior in time and/or space.
In the agro-environmental domain, more and more sensors are recording the manifestations and the evolution of natural phenomena.
The data, recorded as time series, are used to design or to confirm scientific theories that explain the behavior of eco-systems.
For scientists, the difficulty of data analysis rises with the size of recorded data.
They are particularly desiring tools that could facilitate the emergence of interesting features from recorded data, e.g. a relevant regularity or divergence.
Problem
Interesting phenomena emerge more or less obviously in times series with respect to the selected abstraction level: some phenomenon may appear clearly when data are abstracted week by week whereas it is difficult to observe the same phenomenon at the day or month level.
Moreover, the optimal abstraction level evolves during time with the evolution of the context in which the measures are recorded.
In addition, when several sensors record different aspects of the same phenomenon, the measures are often correlated.
These correlations should be displayed to the scientists, such as the temporal causality, e.g. "when the value of some variable rises the value of some other variable decreases with a delay of 3 to 5 days".
Objective
The goal of this project is to design new methods for extracting multi-scale and multi-variate temporal patterns coming from different sensors.
This goal rises some difficult issues. What are the main scales present in the data? What are the relationships between these scales? What are the relationships between the different variables? How to model these relationships and reason with this model?
Tasks
• analyze symbolic and numerical representation proposals for multi-scale time series,
• analyze machine learning and temporal data mining methods of multi-scale multi-variate time series,
• propose algorithms for the simultaneous extraction of multi-scale and multi-variate temporal patterns from several time series,
• implement a prototype of the proposed algorithms,
• assess the proposal on artificial data and on a real dataset provided by INRA recording water quality at the exit of a watershed. The results will be discussed with expert in the agro-environmental domain.
Profile
• PhD in computer science, preferably with a speciality in data mining or symbolic or statistical machine learning
• knowledge in time series analysis, if possible
• good programming skills
Bibliography
[1] Euzenat J., An algebraic approach to granularity in time representation, Proc. 2nd IEEE international workshop on temporal representation and reasoning (TIME), pp 147-154, 1995.
[2] Castro N., Azevedo P., Multiresolution Motif Discovery in Time Series, in Proceedings of the SIAM International Conference on Data Mining (SDM 2010), 2010, pp. 665-676.
[3] Thomas Guyet; René Quiniou. Extracting temporal patterns from interval-based sequences, in International Joint Conference on Artificial Intelligence (IJCAI), Jul 2011, Barcelone, Spain
[4] Shahar Y, Musen MA., Knowledge-based temporal abstraction in clinical domains. Artif. Intell. Med. 1996 Jul;8(3):267-98.
Keywords
machine learning ; data mining ; temporal patterns ; multi-scale ; multi-variate ; time series
Contacts
René Quiniou (INRIA/IRISA - rene.quiniou@inria.fr) Thomas Guyet (Agrocampus Ouest/IRISA - thomas.guyet@agrocampus-ouest.fr) Alice Aubert (INRA - Alice.Aubert@rennes.inra.fr)
Re: Post-doc positions in data mining
Posted by:
webmasterphilfv
Date: September 14, 2012 04:20PM
A good site announcing post-doc positions in data mining:
http://jobs.phds.org/data-mining-jobs/postdoc
http://jobs.phds.org/data-mining-jobs/postdoc