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Masters in Data Mining at Erasmus Mundus

Masters Program for International Students:  Erasmus Mundus Master in Data Mining and Knowledge Management  approved by the ANECA (National Agency for Quality Assessment and Accreditation)

The primary social and economic value of modern societies is knowledge. For this reason, mastering Information technology for structured or unstructured information recorded in Datawarehouses, such as the Web, and containing dormant knowledge is essential to the development of both individual and society.

Data mining and knowledge management (DMKM) has become essential for improving the competitiveness of businesses and increasing access to knowledge. DMKM still comes up, however, against major scientific and technological obstacles. This EM MC’s degree in DMKM proposes specialized training in this field.

The consortium is composed of six universities among four countries: France (University of Pierre and Marie Curie Paris 6, University of Lyon Lumière Lyon 2, Polytec’Nantes), Romania (University Polithenica of Bucharest), Italy (University of East Piedmont) and Spain (Technical University of Catalonia). The Master in DMKM is based on experience in multi-site teaching gained from the Master’s degree in Knowledge Extraction from Data, which has been running since 1999 within three members of the consortium.

Admission criteria:

To be eligible to apply for the Master’s programme DMKM, students must satisfy to both of the following conditions:

* Be the holder of a Bachelor’s degree (a minimum of three years’ study at a university and corresponding to the equivalent of 180 ECTS) in the fields of Computer Science, Mathematics or Statistics:
o When applying, since visa process takes a long time, for Students living in third countries.
o By July, 15th, For Students living in EU.
* Mastering English at a level equivalent to 550 TOEFL.
On this site, a table with the conversion rate between TOEFL International score and other English proficiency scale is reported. More precisely, the minimum scores required to be acceptable for the Master are the following ones:
o TOEFL International (Paper) ? 550
o TOEFL Computer ? 213
o TOEFL IBT ? 79
o IELTS ? 6.0
o TOEIC ? 690
o VEC Online ? 74
o CPE graded B

The Master in DMKM is aimed at students from all over the world. Candidates must have a Bachelor’s degree (or equivalent) in the fields of computer science, mathematics or statistics, as well as a good level of English (TOEFL 550 or equivalent). Admission is granted on the basis of a selection procedure. Classes are taught in English and the course is composed of 18 modules of around sixty hours each. The course runs over 4 semesters.

The first semester is devoted to basic training and includes subjects in mathematics, statistics, databases, etc., whereas the two following ones are devoted to acquire two specialties among six ones, such as Complex Data Mining, Information retrieval, Knowledge acquisition and management, etc. The fourth semester is targeted to the writing of a dissertation in either a laboratory or a company. Language classes will also be provided to ensure that students integrate as well as possible in socio-cultural terms in the host country. Each student must spend (6-12) months in at least two of the four countries.

Students that have obtained 120 ECTS will automatically obtain national Master degrees from the countries in which they have studied. Classes are transmitted via video-conferencing. They are also recorded and accessible online. Students can benefit, on their site of residence, from support in the form of tutoring for each course. Tuition fees are 4,000 €/year for European students and 8,000 €/year for other students. All candidates may apply for Erasmus Mundus scholarships.

Contact detail: abdelkader.zighed@univ-lyon2.fr

Deadlines for application are:

January, 3rd, 2011 at 12:00 (GMT+2) for Non-European candidates
April, 15th, 2011 at 12:00 (GMT+2) for European Students

Apply Here

Research Scientist, Centre for Population Health Sciences, UK

This post is designed to provide statistical research assistance for a project funded by the Chief Scientist’s Office supporting phase 3 of the Scottish Health and Ethnicity Linkage Study, which is focusing on respiratory and gastrointestinal data, and assessing the potential to link primary-care response to data.

In close collaboration with the co-applicants and under the direction of the principal investigator and the project research fellow, the post holder will work to achieve the academic aims and objectives of the grant proposal:

* Establish and explore the health status of minority ethnic groups in Scotland, with emphasis on ethnic inequalities in respiratory and gastrointestinal health.
* Establish the experience of minority ethnic groups regarding access to, and utilisation of, available healthcare services, and quantify any inequalities.
* Demonstrate whether or not NHS services are subject to inequalities and either highlight where action is required or provide evidence that no such action is needed.
* Determine the health outcomes experienced by minority ethnic groups, with emphasis on inequalities.
* Understand the mediators of ethnic inequalities, in particular the contribution of additional factors such as religion, country of birth and socio-economic position in modifying the relationship between ethnic group and health status.

Problem Solving

The post holder is expected to lead in resolving most statistical problems, but in communication with head statistician at ISD and the existing research fellows.

Person Specification

Qualifications: An undergraduate degree or equivalent, with a standard equivalent to an upper second honours degree (2.1) or higher and a masters degree, or equivalent, in a statistical discipline, preferably medical statistics.

Experience: Candidates should, ideally, have some work experience analysing data in a relevant area, such as medicine, nursing, public health, statistics, epidemiology, and social sciences. An interest in the health and health care of ethnic minority groups is necessary, although past experience of working with ethnic minority groups is not.

More specifically we will be looking for:

* Quantitative skills in terms of data preparation and data analysis.

Application Deadline: 17 September 2010

For further scholarship information

Postdoctoral Research Associate in Computer Vision, UK

The School of Informatics has been awarded funding from the EC for participation in a multi-site research project entitled Fish4Knowledge: “Supporting humans in knowledge gathering and question answering w.r.t. marine and environmental monitoring through analysis of multiple video streams”. The principal investigator on the Edinburgh component of the project that is responsible for process modelling and workflow execution is Dr. Jessica Chen-Burger. The project is funded from October 1, 2010 until September 30, 2013.

The research proposed here will take place in the AIAI unit of the Centre of Intelligent Systems and their Applications. CISA undertakes basic and applied research and development in knowledge representation and reasoning. Through its Artificial Intelligence Applications Institute (AIAI) it works with others to deploy the technologies associated with this research. AIAI specialises in Intelligent Systems – systems making use of the knowledge of experts, or systems that learn.

The research proposed here is funded under the EC’s Framework 7 ICT programme (Intelligent Information Management). Fish4Knowledge is a STREP (Specific Targeted Research Project).

The study of marine ecosystems is vital for understanding environmental effects, such as climate change and the effects of pollution, but is extremely difficult because of the inaccessibility of data. Undersea video data is usable but is tedious to analyse (for both raw video analysis and abstraction over massive sets of observations), and is mainly done by hand or with hand-crafted computational tools. Fish4Knowledge will allow a major increase in the ability to analyse this data: 1) Video analysis will automatically extract information about the observed marine animals which is recorded in an observation database. 2) Interfaces will be designed to allow researchers to formulate and answer higher level questions over that database.

The project will investigate: information abstraction and storage methods for reducing the massive amount of video data (from 10E+15 pixels to 10E+12 units of information), machine and human vocabularies for describing fish, flexible process architectures to process the data and scientific queries and effective specialised user query interfaces. A combination of computer vision, database storage, workflow and human computer interaction methods will be used to achieve this.

The project will use live video feeds from 10 underwater cameras as a test-bed for investigating more generally applicable methods for capture, storage, analysis and querying of multiple video streams. We will collate a public database from 2 years containing video summaries of the observed fish and associated descriptors. Expert web-based interfaces will be developed for use by the marine researchers themselves, allowing unprecedented access to live and previously stored videos, or previously extracted information. The marine researcher interface will also allow easy formulation of new queries. Extensive user community evaluations will be carried out to provide information on the accuracy, ease and speed of retrieval of information.

Project Environment and Conditions
The Edinburgh portion of the project will normally not use any specialised equipment. The image capture and supercomputer based processing are primarily NARL’s responsibility. A 500+ node compute server parallel system is also accessible by the group. Wherever possible we will use either MATLAB and C/C++ within a LINUX/UNIX environment (mainly for speed). There is some existing software related to this project. Altogether, there are 10 PCs available for use by the vision research group (consisting of about 10 members, including contract research staff, PhD and MSc students).

The UEDIN workflow team will have to liaise closely with other teams, particularly with the UCATANIA image processing team over the interaction and use of their fish detection and tracking software, the UEDIN machine vision team over the interaction and use of their fish recognition system, the CWI team over the development of properties suitable for question answering and the interaction with their systems, and the NARL team over the development of parallel workflow execution algorithms.

Application Deadline: 5th October 2010

For further scholarship information