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Collaborative Research Center SFB 876 - Providing Information by Resource-Constrained Data Analysis


The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.



Research Assistant (m/f) for Machine Learning

With more than 6,200 employees in research, teaching and administration and its unique profile, TU Dortmund University shapes prospects for the future: The cooperation between engineering and natural sciences as well as social and cultural studies promotes both technological innovations and progress in knowledge and methodology. And it is not only the more than 33,500 students who benefit from that.

The Faculty for Computer Science at TU Dortmund University, Germany, is looking for a

Research Assistant (m/f)

with a strong background in Machine Learning/Data Mining, to start at the next possible date and for the duration of up to three years.

Salary will be paid, in agreement with the lawful regulations of tariffs, according to salary group E13 TV-L resp. according to the provisional regulations of the TVÜ-L, if applicable. The position is a full time appointment; it is in principle suitable for part-time employment too. Duration of the contract will be based on the targeted qualification (e.g. PhD).

Profile:
The Department of Artificial Intelligence at Dortmund is a small team that is involved in international research on Machine Learning and Data Mining, and develops application-oriented theories as well as theoretically well-founded applications. We expect:
• The candidate must have a university master degree in computer science
• Motivation to push research forward
• Interest in exchanging ideas within the team and with international researchers
• Excellent software development skills
• Ability to supervise and motivate students
• Outstanding performance resulting in publications

Tasks:
Responsibilities include teaching (four hours per week, e.g. tutoring, project groups, supervision of students) and support of research on machine learning. Participation at the collaborative research center SFB 876 is expected.

We offer:
• Participation in an inspiring, highly motivated team
• Support in developing the candidate's specific scientific strengths and qualification
• Opportunity to obtain a Ph.D.

The TU Dortmund University aims at increasing the percentage of women in academic positions in the Department of Computer Science and strongly encourages women to apply.

Disabled candidates with equal qualifications will be given preference.

ResearchAssistant_w39-16.pdf

Solving Large Scale Learning Tasks - Essays dedicated to Katharina Morik on the occasion of her 60th birthday

Festschrift Solving Large Scale Learning Tasks

In celebration of Prof. Dr. Moriks 60th birthday, the Festschrift ''Solving Large Scale Learning Tasks'' covers research areas and researchers Katharina Morik worked with. This Festschrift has now been published at the Springer series on Lecture Notes in Artificial Intelligence.

Official presentation of the Festschrift will be on 20th of October at auditorium E23 at Otto-Hahn-Str. 14 starting 16.15 o’clock.

Articles in this Festschrift volume provide challenges and solutions from theoreticians and practitioners on data preprocessing, modeling, learning and evaluation. Topics include data mining and machine learning algorithms, feature selection and creation, optimization as well as efficiency of energy and communication. Talks for the presentation of the Festschrift are: Bart Goethals: k-Morik: Mining Patterns to Classify Cartified Images of Katharina, Arno Siebes: Sharing Data with Guaranteed Privacy, Nico Piatkowski: Compressible Reparametrization of Time-Variant Linear Dynamical Systems and Marco Stolpe: Distributed Support Vector Machines: An Overview.

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DockHa - Personal Hadoop cluster on Docker Swarm in minutes

Analysing Big Data typically involves developing for or comparing to Hadoop. For researching new algorithms, a personal Hadoop cluster, running independently of other software or other Hadoop clusters, should provide a sealed environment for testing and benchmarking. Easy setup, resizing and stopping enables rapid prototyping on a containerized playground.

DockHa is a project developed at the Artificial Intelligence Group, TU Dortmund University, that aims to simplify and automate the setup of independent Hadoop clusters in the SFB 876 Docker Swarm cluster. The Hadoop properties and setup parameters can be modified to suit the application. More information can be found in the software section (DockHa) and the Bitbucket repository (DockHa-Repository).

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