9. – 20.09.2024

TUM School of Management Doctoral Summer School 2024

In 2024, we again want to welcome many doctoral candidates to Heilbronn for the third TUM School of Management Doctoral Summer School. From September 9, to September 20, 2024 this years’ Summer School will take place, spread over the course of two weeks with two courses per week. The excellent offer of doctoral courses will be accompanied by great evening sessions and plenty of time for networking with the professors and your peers.

 

The Doctoral Summer School understands itself as an explicitly interdisciplinary endeavour and is therefore aimed at doctoral candidates from all kinds of disciplines and especially from all TUM School of Management campuses.

 

If you have any questions before applying, please contact: doctoral_summer_school@mgt.tum.de 

Courses

  • Lecturer: Prof. Dr. Christoph Ann (TUM)

     

    The course will cover the theoretical aspects of the module in a discussion with the lecturer. It will also provide the opportunity to work individually or in groups on case scenarios, covering issues of technology protection and patent law. The purpose is to repeat and to intensify the content discussed in course meetings and to review and evaluate legal issues from different
    areas of law in everyday situations.


    The course is designed to provide doctoral candidates with an understanding of how technology protection works and the stakeholders involved. Topics covered are:

    • Technology protection?
    • Why and how best?
    • Patent quality & patent trolls
    • Standard essential patents (SEPs) & FRAND licensing
    • Patent courts & patent offices
    • From Patents to profits

    SyllabusTechnology protection for Doctoral Candidates

  • Lecturer: Prof. Dr. Robert Graf (International University of Applied Sciences)

     

    Knowledge Objectives
    Doctoral candidates will understand key concepts related to conditionality of probability. They will
    comprehend key theorems and be able to follow core ideas of proofs. They will be able to connect different results.
    Skills Objectives
    Doctoral candidates learn how to derive results formally. They will also learn to transfer abstract and fundamental theorems to their research questions. They will further be able to build on fundamental theorems in their research.
    Learning Objectives
    Doctoral candidates will learn how to read mathematical texts and proofs. They will learn to state technical results in English, that is, in their own words. In doing so, they will learn how to rephrase technical expressions without losing rigor.

     

    SyllabusConditionality in probability

  • Lecturer: Prof. Dr. G.P. Kiesmüller (TUM) 

     

    Many real life systems are subject to uncertainty and should therefore be modelled with stochastic models. In this course, we focus on the theory and the application of Markov Decision Processes and Semi Markov Decision Processes. The students should gain knowledge about these models such that they are able to construct these models and apply them to solve real life problems. For illustration, we use among others, models of inventory systems, manufacturing systems and maintenance systems. We practice to derive the Bellmann equation for these systems and show how an optimal solution can be computed numerically. Besides the traditional solution approaches, we also discuss approaches based on reinforcement learning.

     

    Syllabus: Markov Decision Processes

  • Lecturer: Prof. Dr. Johannes Resin (TUM)

    We will discuss

    • concepts such as forecast calibration (reliability) and sharpness (discrimination);
    • various types of forecasts:
      • probabilistic (density) forecasts for uncertainty quantification,
      • point predictions and how to make sense of them despite forecast uncertainties,
      • other types of forecasts such as prediction intervals conveying uncertainty;
    • tools to assess calibration such as PIT histograms and reliability diagrams;
    • summary measures of forecast performance provided by
      • proper scoring rules,
      • consistent scoring or loss functions,
      • score decompositions;
    • regression, as well as model diagnostics, from a predictive perspective;
    • combination of predictive distributions.

    The course enables doctoral candidates to apply these tools in their own research to identify good forecasts, as well as potential short-comings in poor forecasts. The practical relevance of the course materials will be illustrated throughout using multiple case studies.

     

    Syllabus: Probabilistic Forecasting: What Makes a Good Forecast?

PROGRAM SCHEDULE

HOW TO APPLY

Please apply using the digital registration form on this website. The application form will be visible on June 3, 2024, 12:00 noon. The places will be distributed on a first come - first served basis. Registration is only possible for doctoral candidates of TUM School of Management.

 

Application begin: June 3, 2024, 12:00 noon
Application end: June 30, 2024, end of day

TRAVEL AND ACCOMODATION

Travel costs, accomodation, the Welcome Breakfast and Lunch throughout the week will be covered for all doctoral candidates of TUM School of Management (internal and external). Dinner and all other costs have to be paid by the participants themselves. More information will be provided after successfull registration. Hotel rooms are already reserved for you. If you need accommodation for your time in Heilbronn please let us know on the application form. 


The Summer School starts on Monday morning, so please plan your travels accordingly.

 

Participation in the evening events is expected.

Titel*

Titel*

ChevronDownRegular
Internal doctoral candidate (contract at TUM) / External doctoral candidate (no contract at TUM*

Internal doctoral candidate (contract at TUM) / External doctoral candidate (no contract at TUM*

ChevronDownRegular
Need of accommodation in Heilbronn*

Need of accommodation in Heilbronn*

ChevronDownRegular
I want to register for the following course in week 1:

I want to register for the following course in week 1:

ChevronDownRegular
I want to register for the following course in week 2:

I want to register for the following course in week 2:

ChevronDownRegular
Both courses take place at the same time. If my application for the chosen course is not successful, I want to be considered for the other course*

Both courses take place at the same time. If my application for the chosen course is not successful, I want to be considered for the other course*

ChevronDownRegular