PhD in Deep Federated Learning with Medical Imaging (Robustness and Explainability)

Updated: about 4 hours ago

03.06.2021, Wissenschaftliches Personal

The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and quality control tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep learning to push our understanding of the robustness and explainability of Federated Learning models.

Your responsibilities:

  • Build and create clinical use-cases for benchmarking existing state-of-the-art (SOTA) Federated Learning algorithms. This includes running a few pre-processing pipelines.

  • Develop SOTA algorithms in Robustness against data and model poisoning attacks.

  • Develop SOTA algorithms in the Explainability of Deep Federated models.

  • Publish and present scientific results at international conferences and high-impact journals.

  • Close collaboration with team members and colleagues.

Essential qualifications:

  • M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning.

  • Strong knowledge in Machine/Deep Learning with experience in discriminative models, adversarial attacks, and Bayesian neural networks.

  • Excellent analytical, technical, and problem-solving skills

  • Excellent programming skills in Python and PyTorch including fundamental software engineering principles and machine learning design patterns.

  • Be highly motivated and a team player with excellent communication and presentation skills, including experience in communicating across discipline boundaries.

  • Fluent command of the English language

Desirable qualifications:

  • Track record of publications at top-tier conferences and high-impact journals in the field

  • Hands-on experience with Federated Learning frameworks.

  • Hands-on experience with MONAI framework.

  • Working in a Linux environment, with experience of shell scripting, cluster, or cloud computing.

  • Fluency in spoken and written German.


We are looking forward to receiving your comprehensive online application until 20 June 2021. Please send your application (in English) in a single PDF file – including:

a) Motivation letter: Describe the reason for applying to be part of our lab, and why you think you are the right candidate to fill this position (max. 2 pages).

b) Curriculum vitae incl. list of publications.

c) copy of your diploma/degree certificates.

d) At least two reference letters (or the names of two referees).

Interested? Please send your application via email with the subject “AlbarqouniLab_PhD_Robustness” to Shadi Albarqouni, shadi.albarqouni@helmholtz-muenchen.de.


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: shadi.albarqouni@helmholtz-muenchen.de


More Information

https://albarqouni.github.io/team/


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