PhD/Postdoc Position (m/f/d) in Numerical Mathematics

19.11.2024, Wissenschaftliches Personal

TBD ...

Project Outline

The project focuses on the numerical simulation of blood flow within cerebral aneurysms. Arterial geometries are derived from medical scans (e.g., CT) of real patients, which are suitably meshed and processed for numerical treatment using Lattice-Boltzmann methods (LBM). For fluid simulations, we utilize the high-performance LBM framework waLBerla, predominantly written in C++, but increasingly adapted for GPU computations through automatic code generation using Python scripts. In addition to simulating the current state of an aneurysm, the project also examines the long-term outcomes of treatments, including coiling devices, medical micro-wires inserted into the aneurysm, where they coil up and ultimately seal off the aneurysm from blood flow. Aspects such as thrombus formation, inflammatory processes in the vessel wall, and its pulsation are as significant as post-implantation deformations of the devices and their influence on blood flow dynamics. Alongside computationally intensive, fully resolved simulations, the project employs porous media surrogate models and their LBM implementation. Given that numerous measurements and parameters are subject to uncertainties, the project also incorporates uncertainty quantification (UQ) with the ultimate goal of providing at least statistical insights into the risks and success rates of real, patient-specific aneurysms, their treatment options, and long-term prognosis. The project is complemented by contributions in machine learning, such as the rapid generation of realistic implant geometries or the learning of biomedical parameters from experimental or clinical datasets.

Links:

• SPP2311 Webpage
• Chair for Numerical Mathematics Webpage
• Project Webpage
  

Responsibilities: The specific tasks within the project include

• Mathematical derivation, analysis, and comparison of models, methods, and simulation approaches.

• Rapid prototyping of new ideas in custom code.

• Implementation of new models, methods, and algorithms into an existing framework, with a focus on efficiency.

• Creation and execution of relevant simulation pipelines: from real data to mathematical and clinically actionable results.

• Publication of results in the scientific community (journals, conference contributions, lecture presentations, etc.) in English.

Requirements: If you bring:

• A completed degree (Master's) in Applied Mathematics, CSE, or comparable programs with above-average results.

• Good programming skills and experience in C++ and Python.

• Knowledge (acquired during a Master's program) in numerical methods and simulation, particularly for partial differential equations.

• Basic knowledge in mathematical modeling with/and partial differential equations, with a focus on fluid or biomechanics, porous media.

• Optional/advantageous: Experience with Lattice-Boltzmann methods and their implementation, as well as high-performance computing experience.

We Offer: You can expect:

• A dynamic and international team of scientists from various disciplines, as well as collaboration with international partners.

• An exciting and diverse project with numerous aspects and opportunities for development.

• Modern hardware and infrastructure at the workplace, ranging from compute and GPU servers to supercomputers.

• Project-based work towards a doctoral degree (Dr. rer. nat.) at TU Munich based on the project topic.

• Salary according to public service tariffs, pay group E13.

Application: Have we sparked your interest in the project and the position?
Then apply by referring to this job posting via email to:
Prof. Dr. Barbara Wohlmuth: wohlmuth@cit.tum.de
Please include a compelling cover letter, your CV, and your final transcript of records from your most recent educational phase (e.g., degree or Ph.D.). If you have relevant publications (or, for example, a Master’s thesis) that you would like to be considered as part of your application, feel free to send those along as well.

Application Deadline: Monday, December 16, 2024

You also have the option to send your application documents by physical mail to the address provided below. Please note that submitted documents will not be returned after the application process is completed, but will be destroyed in compliance with data protection regulations.

Address:

Lst. für Numerische Mathematik (Hauspost M2)

School of Computation, Information and Technology

Technische Universität München

Boltzmannstraße 3

D-85748 Garching

Severely disabled candidates will be given preferential consideration if they have essentially the same suitability and qualifications.

TUM aims to increase the proportion of women, and applications from women are therefore explicitly encouraged.

Privacy Notice:

As part of your application for a position at the Technical University of Munich (TUM), you will submit personal data. Please take note of our privacy notice in accordance with Art. 13 of the Datenschutz-Grundverordnung (DSGVO) regarding the collection and processing of personal data in the context of your application:

https://portal.mytum.de/kompass/datenschutz/Bewerbung/

By submitting your application, you confirm that you have read and understood TUM's privacy notice.

Kontakt: Prof. Dr. Barbara Wohlmuth: wohlmuth@cit.tum.de

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