PhD position in machine learning with linear operators

Updated: 3 months ago

11.01.2022, Wissenschaftliches Personal

We are looking for two motivated researchers with a mathematics or informatics background, to work on machine learning and linear operator theory. Their projects will be embedded in the Emmy Noether research group by Dr. Felix Dietrich, at the chair of Scientific Computing in Computer Science of TUM. Remuneration is 100% TVL E13 according to the German public sector rates. The candidates have the opportunity to pursue a doctoral degree (Ph.D.), and will be working together in the same research group.

About Us
Located in the prosperous capital of Bavaria and home to over 39000 students, the Technical University of Munich (TUM) is one of the world’s top universities. It is committed to excellence in research and teaching, interdisciplinary education, and the active promotion of promising young scientists. TUM benefits from the healthy mix of companies and startups of all sizes headquartered in the region and is tightly connected to regional research hospitals. The university also forges strong links with companies and scientific institutions across the world.
We are looking for two motivated researchers. Their projects will be embedded in the Emmy Noether research group by Dr. Felix Dietrich, at the chair of Scientific Computing in Computer Science of TUM. Remuneration is 100% TVL E13 according to the German public sector rates. The candidates have the opportunity to pursue a doctoral degree (Ph.D.), and will be working together in the same research group.

Description
You can apply to two PhD projects devoted to explainability of AI by bridging the gap from machine learning to rigorous mathematics. Using and advancing the commonality between the Laplace operator, Gaussian processes, and neural networks, the first project connects AI and linear algebra. This will allow us to explain data representation and domain adaptation algorithms. The second PhD project is devoted to the linear Koopman operator and its connection to the Neural Tangent Kernel. The main objective in this project is to further understanding of iterative training and data processing in machine learning. The project description attached below contains more details.

Requirements

  • Master in Informatics, Mathematics, or related.
  • Knowledge about Machine Learning (neural networks, Gaussian processes), Scientific Computing (linear operators, matrix approximations).
  • Preferred: Experience with TensorFlow, PyTorch, or similar software.
  • Soft skills: analytical thinking, structured and organized work, high intrinsic motivation.

How to apply?
Send an email with subject "PhD position: linear operators" to phdapplications@mailsccs.in.tum.de no later than 31.01.2022 EOD, and attach the following (all in one PDF):

  • 1/2 page of motivation letter: Why do you want to pursue a Phd, why in this field of research?
  • CV including education, (optional) industry experience, and (optional) completed projects.
  • Transcript of records (completed courses and grades).
  • Names of one or two contact persons (we will ask them for letters of reference).

You can also send your documents by post to the address of Dr. Felix Dietrich below.

Note that TUM has been pursuing the strategic goal of substantially increasing the diversity of its staff. As an equal opportunity and affirmative action employer, TUM explicitly encourages nominations of and applications from women as well as from all others who would bring additional diversity dimensions to the university’s research and teaching strategies. Preference will be given to disabled candidates with equal qualifications. International candidates are also highly encouraged to apply.

Contact
If you have any questions about the application or the project, write an email or use the contact form on the website:

Dr. Felix Dietrich
TU Munich
Institute for Informatics
Boltzmannstr. 3
85748 Garching b. München

phdapplications@mailsccs.in.tum.de
www.fd-research.com/contact
Link to the chair homepage

Data Protection Information:
When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.

Kontakt: Dr. Felix Dietrich, phdapplications@mailsccs.in.tum.de


More Information

https://www.in.tum.de/en/news-single-view-en/article/neue-emmy-noether-forschungsgruppe-um-dr-f-dietrich-bewilligt0/


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