Higher Education

Statistical Learning and Data Science - Ludwig Maximilian University of Munich - Munich Center for Machine Learning

1-10

Germany

**Join the Interpretable Machine Learning & Explainable AI research group, which is part of the Chair of Statistical Learning and Data Science at the Ludwig-Maximilians-Universität (LMU) München, led by Prof. Dr. Bernd Bischl who is also one of the directors of the Munich Center for Machine Learning (MCML), a leading competence center designed to consolidate machine learning activities in Munich.**

**Project description:**

**The main research will focus on the advancement of interpretation methods for machine learning (ML) models trained on tabular data. Existing interpretation methods produce either local explanations for insights into individual observations or global explanations that characterize the overall behavior of a model. During your Ph.D. journey, you will conduct research on a wide range of interpretation methods, including regional explanations, which strike a balance between local and global interpretability, and other innovative methods aimed at enhancing the overall explainability of ML models.**

**How to apply (please prepare a single PDF file firstname\_lastname.pdf containing the following contents):**

*   **Letter of motivation stating your preferred starting date (max. 1 page).**
*   **Detailed CV including your programming skills.**
*   **A list of courses you have attended in statistics and machine learning with a brief overview of the most important topics covered within each course.**
*   **Certificates and transcripts of records of all university degrees obtained.**
*   **International applicants should include proof of English language proficiency (e.g. TOEFL, IELTS, ...).**
*   **Please apply until the 1st of October 2024.**

## Tasks

**Your Responsibilities:**

*   **Conduct research at the intersection of machine learning and statistics to improve the interpretability of predictive models trained on tabular data.**
*   **Publication of scientific results in internationally renowned journals and their presentation at international top-tier conferences and workshops.**
*   **Collaborate with fellow researchers, actively contributing to research projects and/or open-source software projects. Assistance in teaching tasks and the development of course material for machine learning-related classes at the LMU such as Introduction to Machine Learning or Interpretable Machine Learning.**

## Requirements

**Your profile:**

*   **M.Sc. in statistics, mathematics, data science, computer science, or related discipline.**
*   **Strong theoretical knowledge in both machine learning and statistics is essential.**
*   **Proficient programming skills in R and/or Python.**
*   **Knowledge or experience in interpretable machine learning (especially model-agnostic methods) is advantageous but not required.**
*   **Proficient in both spoken and written English, with strong communication skills.**
*   **A basic understanding of German is a plus, but not required.**

## Benefits

**What we offer:**

*   **Fully funded 3-year position with potential for extension.**
*   **Interesting research projects in an exciting and evolving field.**
*   **A supportive scientific environment within a top-ranked German university.**
*   **Opportunities for international networking and exchange.**
*   **Comprehensive support and close collaboration with experienced researchers, including intensive supervision, guidance, and mentoring to support your success and development in and during your PhD journey.**

Employee

Join the Interpretable Machine Learning &amp; Explainable AI research group, which is part of the Chair of Statistical Learning and Data Science at the Ludwig-Maximilians-Universität (LMU) München, led by Prof. Dr. Bernd Bischl who is also one of the directors of the Munich Center for Machine Learning (MCML), a leading competence center designed to consolidate machine learning activities in Munich.
Project description:
The main research will focus on the advancement of interpretation methods for machine learning (ML) models trained on tabular data. Existing interpretation methods produce either local explanations for insights into individual observations or global explanations that characterize the overall behavior of a model. During your Ph.D. journey, you will conduct research on a wide range of interpretation methods, including regional explanations, which strike a balance between local and global interpretability, and other innovative methods aimed at enhancing the overall explainability of ML models.
How to apply (please prepare a single PDF file firstname_lastname.pdf containing the following contents):
<ul>
<li>Letter of motivation stating your preferred starting date (max. 1 page).</li>
<li>Detailed CV including your programming skills.</li>
<li>A list of courses you have attended in statistics and machine learning with a brief overview of the most important topics covered within each course.</li>
<li>Certificates and transcripts of records of all university degrees obtained.</li>
<li>International applicants should include proof of English language proficiency (e.g. TOEFL, IELTS, ...).</li>
<li>Please apply until the 1st of October 2024.</li>
</ul>
<h2 id="tasks">Tasks</h2>
Your Responsibilities:
<ul>
<li>Conduct research at the intersection of machine learning and statistics to improve the interpretability of predictive models trained on tabular data.</li>
<li>Publication of scientific results in internationally renowned journals and their presentation at international top-tier conferences and workshops.</li>
<li>Collaborate with fellow researchers, actively contributing to research projects and/or open-source software projects. Assistance in teaching tasks and the development of course material for machine learning-related classes at the LMU such as Introduction to Machine Learning or Interpretable Machine Learning.</li>
</ul>
<h2 id="requirements">Requirements</h2>
Your profile:
<ul>
<li>M.Sc. in statistics, mathematics, data science, computer science, or related discipline.</li>
<li>Strong theoretical knowledge in both machine learning and statistics is essential.</li>
<li>Proficient programming skills in R and/or Python.</li>
<li>Knowledge or experience in interpretable machine learning (especially model-agnostic methods) is advantageous but not required.</li>
<li>Proficient in both spoken and written English, with strong communication skills.</li>
<li>A basic understanding of German is a plus, but not required.</li>
</ul>
<h2 id="benefits">Benefits</h2>
What we offer:
<ul>
<li>Fully funded 3-year position with potential for extension.</li>
<li>Interesting research projects in an exciting and evolving field.</li>
<li>A supportive scientific environment within a top-ranked German university.</li>
<li>Opportunities for international networking and exchange.</li>
<li>Comprehensive support and close collaboration with experienced researchers, including intensive supervision, guidance, and mentoring to support your success and development in and during your PhD journey.</li>
</ul>

PhD Position in Interpretable Machine Learning and Explainable AI (m/f/d)

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