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Schedule Current Topics in Data Engineering 2019 

Tu   3. 9.  2019   9:45    Stefan Kettemann,  Introduction,  Lecture Hall Research III

Fr    13. 9.            9:45  Katja Windt,  Dirk Lieftucht, SMS  Digital  Lecture Hall Research II

SMS Data Challenge 3.0

Data Science meets Steel Industry – an exciting match of heavy metal and high tech

for details click on the DE Blog


Tu 17. 9. 2019    9:45  Marc-Thosten Hütt, Bio-Informatics and Systems Biology, Lecture Hall Research III


Fr  20. 9.           9:45  Peter Zaspel, Machine Learning in Scientific Computing,   Lecture Hall Research II

Further references:

Tu 24. 9. 2019   9:45 Agostino Merico, Analysis of Complex Ecological and Social Systems, Lecture Hall Research III    (For Presentation slides see child page on the left)           

Fr  27. 9.           9:45    Peter Baumann, Analytics on Big Data Cubes in Science and Engineering  Lecture Hall Research II 

Tu 1. 10. 2019    9:45   Lecture Hall Research III    Tutorial  on  Writing the term paper and Preparing  the Poster  Lecture Hall Research III

Fr 4.10.             9:45      Lecture Hall Research II      no lecture

Tu 8. 10. 2019    9:45 Lecture Hall Research III   Adalbert F. X. Wilhelm  Feature engineering for sensor data


Fr  11.10.          9:45   Lecture Hall Research II  no lecture

Tu 15. 10. 2019    9:45 Lecture Hall Research III: Vikram Unnithan, Angelo Pio Rossi, Joachim Vogt,  Earth and Space Sciences: Big Geo-data - link to slides HERE

Fr  18.10.          9:45  Lecture Hall Research II   Stefan Kettemann, Data Engineering for the Energy Transition 

Tu 22.10.          9:45    Lecture Hall Research III  no lecture

Fr   25.10.           9:45    HR Forecast,  Data Engineering in NLP  Lecture Hall Research II   

 Slides of HR Forecast on NLP from 2019 

Slides from HR forecast  2018: 

Tu   29.10.           9:45   Giuseppe Abreu, Communication Theory Lecture Hall Research III  

Tu  29.10.   14.00-15:30      Sergey Kosov, Machine Learning in Computer Vision  Lecture Hall, R. 53 in Research I

Tu  5.11.      9:45-11:00      Lecture Hall Research III    Tutorial  on  Writing the term paper and Preparing  the Poster

Fr  15.11.    9:45 CSC Infosession for Data Engineering Master Students, Christin Klähn          

  • Why would students need Career Services?
  • What CSC at Jacobs University does 
  • Introduction into Career Advising Seminars 
  • The Internship Program (how to get a voluntary internship)
  • Interview training
  • How to write a CV recommendations     
  • Career Center Website by JobTeaser
  • Career Events


Tu 19.11.        9:45 Hans Pfeiffenberger, Alfred Wegener Institut für Polar- und Meeresforschung,

Challenges and Chances of  Scientific Data ManagementLecture Hall Research III

Fr  22.11.         12:00-14:00      Poster presentation by Data Engineering students in the  East Wing ICC

                                  



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With a colleague from the faculty of law at the University of Hamburg, I am organizing a small summer school to explore an emerging topic at the interface of data science and law: algorithmic biases and the fairness of algorithms. The summer school will essentially be a four-day joint brainstorming session exploring this topic. Note that due to the small number of slots available for participants we require letters of motivation from each applicant. As background, see: Schubert, C., & Hütt, M.…
------------------------------------------------------------------------------                         Course Announcements ------------------------------------------------------------------------------ Introduction to parallel programming with MPI and OpenMP Online course 12.-15. April 2021 (10:00-16:30) Single-process optimization and single-node performance engineering Online course 19.-20.…
The "Bremen Big Data Challenge" starts for the sixth time on March 1st, 2021! The BBDC is a programming challenge focused on data analysis for all students  at universities in and around Bremen: You have six weeks to develop clever  algorithms and analysis methods to find patterns in labeled data and make  predictions on unseen data. In this year's challenge, you will need to detect  acoustic events in presence of different background noises correctly.…

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