Courses and Workshops for Doctoral Candidates

The MSE Graduate Center regularly offers courses and workshops for doctoral candidates. Attendance is free of charge for all doctoral candidates of TUM!

Some events may only be visible in the German version of this website.

Using R for Regression Analysis

Content:

This learning-by-doing course will show how to use R in the context of the ANOVA model, (generalised) linear regression, ordinal regression and the linear mixed model.

Requirements:

If needed, all participants should refresh their basic statistics and R knowledge by looking at e.g. the book Field, A., Miles, J. and Field Z. (2012). Discovering Statistics Using R. SAGE Publications and/or the DataCamp course www.datacamp.com/courses/free-introduction-to-r.

Participants have to bring their own laptop, which should have R (www.r-project.org) and RStudio (www.rstudio.com) installed on it. The laptop should also be able to connect to the eduroam wlan.

It will take place:

from 28.02. to 01.03.2019 , 9am to 5pm in room 0002 (5414.EG.002), ZEI, Lichtenbergstraße 4 a, 85748 Garching

Lecturer:

The course will be held by Dr. Stephan Haug (TUM)

Participants:

The course will be limited to 15 participants.

The course will be recognized as a subject-specific course by the Graduate Centre MSE.

Please register by sending an e-mail to: graduiertenzentrum@mse.tum.de

Python-Workshop

The graduate center organizes a two-day Python-Workshop for the doctoral candidates of the MSE.

When? Thu-Fr 25.-26.04.2019 from 9:00 to 17:00

Where? ZEI, Room 1002, Lichtenbergstraße 4a, 85748 Garching

Lecturer: Herr Dr. Johannes Dorfner

The participation counts as a specialist qualification by the graduate center.

If the demand is not met, members from other graduate centers of the TUM may participate.

Please sign up (binding) until 31.03.2019 at: graduiertenzentrum@mse.tum.de (graduate center MSE).

Optimization under Uncertainty with Applications in Energy Markets

Content:

The course introduces the principles of decision making under uncertainty with a focus on stochastic optimization and robust optimization. The theory will be complemented by numerous illustrative classical examples such as the newsvendor problem and examples from the field of energy markets. The course consists of a lecture part, where the basic principles from stochastic optimization and robust optimization are presented as well as student presentations. Students either present an academic paper that uses the methods covered in the course or methods that build on the presented material.

Syllabus:

a. Two stage stochastic optimization: concepts and tools
b. Multi-stage stochastic optimization: scenario trees and decomposition
c. Introduction to robust optimization: concepts and standard uncertainty sets
d. Robust counterparts and tractable reformulations.
e. Distributionally Robust Optimization

Tools & Requirements:

- The course is aimed at students with a clearly quantitative focus
- Knowledge about basic linear algebra, probability, and optimization is a plus
- Proficiency in MATLAB or a similar high level programming language is an advantage

Place and date:

It will take place on the downtown campus during the summer semester 2019. Place and dates will be published soon.

Lecturer:

The course will be held by Prof. Wozabal (TUM)

Participants:

The course will be limited to 20 participants.

The course will be recognized as a subject-specific course by the Graduate Centre MSE.

Please register by sending an e-mail to: graduiertenzentrum@mse.tum.de

Modelica-Workshop for doctorate candidates (work in progress)

Time: To be announced.

Place: Room 1002 ZEI / Lichtenbergstraße 4a, 85748 Garching

Content: In this Workshop the basics of the open-source software Modelica are taught. Modelica is an object-oriented modelling language, which can be used to simulate complex physical models from diverse fields of expertise (eg. Mechanics, Electrical engineering, Thermodynamics, Hydraulics, Control technology, Processing). Apart from the more than 1600 model components and 1350 functions freely available, Modelica also features a significant advantage compared to other simulation programs (like Matlab/Simulink), which is its method of operation. Modelica works with differentiable, algebraic and discrete equation systems instead of allocations, which allows for the model equations not needing to be solved for the variables a priori.  The Modelica translator translates physical models (in other words the model equations) on its own and solves them using an algorithm. Open source Modelica is also the basis for extensions that build on top of it, which are used in the industry (eg. automobile industry and power plants).

Sign up: Please sign up per e-mail at: graduiertenzentrum@mse.tum.de.

Participation in this workshop is recognized as a specialized qualification by the graduate centers.

If some vacancies remained, the remaining spots will be given to other TUM graduate centers.

Competent graduation work supervision (taught in german)

Inhalte

Die Betreuung von Abschlussarbeiten nimmt einen Teil der Arbeit von Promovierenden ein und ist eine Aufgabe, die aufgrund der Diversität der Studierenden, Themengebiete und Ziele eine hohe Komplexität aufweist. In diesem Workshop führt das Trainerteam die TeilnehmerInnen sukzessive durch den gesamten Betreuungsprozess und reflektiert mit Ihnen gemeinsam die Betreuungsintensität und –qualität in den einzelnen Etappen – ausgehend von Inputs durch die Trainerinnen sollen die TeilnehmerInnen basierend auf der Reflexion eigener Erfahrungen und dem jeweiligen fachlichen Hintergrund praxisnahe Hilfestellungen erhalten. Zudem gibt es Raum, um an 1-2 konkreten, gegebenenfalls ‚kniffligen‘ Betreuungssituationen zu arbeiten.

Lernziele

Am Endes des Kurses sind die TeilnehmerInnen in der Lage,

• zentrale Faktoren einer erfolgreichen Betreuung von Studienarbeiten zu identifizieren.
• wichtige Etappen der Begleitung einer Studienarbeit zu definieren und den Betreuungsablauf dementsprechend zu strukturieren.
• erste Tools für das professionelle Führen von Beratungs-/Feedbackgesprächen mit Studierenden abzurufen.

Methodik


• Interaktiver Lehrvortrag
• Moderiertes Arbeiten in Kleingruppen
• Selbstreflexion

Leitung Amélie Prebeck, Ellen Taraba (ProLehre | Medien und Didaktik)

Termin 23.04.2019 von 14:00 bis 18:00

Ort München, Augustenstraße 46, Erdgeschoss, S02 Eine Wegbeschreibung finden Sie hier www.prolehre.tum.de/kontakt/wegbeschreibung/ Kurssprache Deutsch

Zielgruppe Promovierende der BGU und MSE

Voraussetzung Keine

Plätze 6-12

Die Teilnahme wird als fachliche Qualifizierung durch das Graduiertenzentrum anerkannt.

Restplätze werden an Mitglieder der weiteren Graduiertenzentren der TUM vergeben.

Bitte melden Sie sich verbindlich bis zum 08.03.2019 an bei graduiertenzentrum@mse.tum.de (Graduiertenzentrum MSE).

 

 

Additional documents