Student/in für ein prozesstechnisches Praktikum gesucht

Ein Industriepartner des SPOTSeven Labs bietet ein drei bis sechsmonatiges Praktikum an.
Gesucht wird ein Student der Studiengänge Verfahrenstechnik, Maschinenbau, Elektrotechnik, Informatik, Mathematik, etc. für ein Praktikum in der Forschung und Entwicklung im Bereich Datenanalyse, Mustererkennung und Maschinelles Lernen.
Vorkenntnisse in der Programmierung (Matlab/Octave o.ä.) sind vorteilhaft.
Nähere Informationen (per Mail) bei Prof. Bartz-Beielstein.

@GECCO2018 Call for Competition Participation

Five competitions are held as part of the 2018 Genetic and
Evolutionary Computation Conference (GECCO 2018). We have a number of competitions ranging from different types of optimization problems to games and industrial problems.

Kyoto, JAPAN
July 15-19, 2018
Overview URL:

Black Box Optimization Competition: 30 June 2018
Competition on Niching Methods for Multimodal Optimization: 30 June 2018
General Video Game AI Competition: 15 June 2018
Internet of Things: Online Anomaly Detection for Drinking Water Quality:
30 June 2018
Two-page abstract are due on 27 March — more information below.
Virtual Creatures Competition: 22 June 2018

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Eröffnung Josef Ressel Zentrum #JRZ “Symbolische Regression”: Keynote von Prof. Bartz-Beielstein #TH_Koeln #AI #ML

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Am 2. März wurde an der FH Oberösterreich das Josef Ressel Zentrum (JRZ) für Symbolische Regression offiziell eröffnet. Thema des Forschungszentrums sind mathematische Modelle für Antriebssysteme. Geforscht wird in Kooperation mit den Unternehmen AVL List und Miba und gefördert durch das Bundesministerium für Digitalisierung und Wirtschaftsstandort.
Das Forschungsteam wird von FH-Prof. DI Dr. Gabriel Kronberger geleitet. In dem Forschungszentrum arbeiten zwei Dissertanten, 1,5 FTE Postdocs sowie weitere Diplomanden
Thematisch ist das JRZ im FH OÖ Center of Excellence for Smart Production angesiedelt.

Prof. Bartz-Beielsteins Keynote behandelte das Thema  „Künstliche Intelligenz in industriellen Anwendungen“. Zwischen den Arbeitsgruppen am JRZ und Prof. Bartz-Beielstein besteht bereits seit mehr als zehn Jahren ein intensiver fachlicher Austausch. So wird an der FH Oberösterreich in der Arbeitsgruppe HEAL u.a. die Software “HeuristicLab” (Heuristic and Evolutionary Algorithms Laboratory) entwickelt. Die Arbeitsgruppe HEAL wird von Prof. Michael Affenzeller geleitet, Prof. Stefan Wagner fungiert als “Head Architect”.
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New paper: #Optimization via multimodel #simulation published in “Structural and Multidisciplinary Optimization”

An online version of the paper “Optimization via multimodel simulation” ( written by Thomas Bartz-Beielstein, Martin Zaefferer, and Quoc Cuong Pham was published today. This research paper was published in the journal “Structural and Multidisciplinary Optimization“.

Increasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation. Several simulation tools can be run independently and in parallel, e.g., long running computational fluid dynamics simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. However, there are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes. At the same time, data-driven models require input and output data, but analytical models do not. The optimization via multimodel simulation (OMMS) approach, which is able to combine results from these different models, is introduced in this paper. We believe that OMMS improves the robustness of the optimization, accelerates the optimization-via-simulation process, and provides a unified approach. Using cyclonic dust separators as a real-world simulation problem, the feasibility of this approach is demonstrated and a proof-of-concept is presented. Cyclones are popular devices used to filter dust from the emitted flue gasses. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities. Pros and cons of this multimodel optimization approach are discussed and experiences from experiments are presented.

Combined simulation Multimodeling Simulation-based optimization Metamodel Multi-fidelity optimization Stacking Response surface methodology 3D printing Computational fluid dynamics

Cite this article as
Bartz-Beielstein, T., Zaefferer, M. & Pham, Q.C. Struct Multidisc Optim (2018).

Publisher Name
Springer Berlin Heidelberg
Print ISSN1615-147X
Online ISSN1615-1488