PhD student

Prof. Dr. Kainz, BernhardDepartment Artificial Intelligence in Biomedical Engineering (AIBE)W3-Professur für Image Data Exploration and AnalysisE-Mail: bernhard.kainz@fau.de

Description

In the research field image-based in-distribution testing and generative AI for autonomous vehicles with a fixed-term full-time employment.

An opportunity for further scientific qualification (PhD) is given and supported. The successful candidate will work joinly with industry on natural image data to find unexpected data in image streams. The aim is to build safeguards for downstream image interpreation models. There is potential to explore self-supervised methods as well as category theory and symmetry-exploiting methods with noisy and missing labels, including work on domain adaption challenges. The successful applicant will be part of the Image Data Exploration and Analysis (IDEA) lab @AIBE FAU, which is the research group of Prof. Bernhard Kainz. We have pioneered new methodological developments in the field of image analysis and machine learning as evident from several international awards, prizes, and best paper awards. The group is internationally renowned for its research in medical deep learning, which has been proved by publications at top conferences (MICCAI, CVPR, ECCV, ICCV) and in prestigious journals (IEEE TMI, Medical Image Analysis, Nature Machine Intelligence, The Lancet, etc.).

Qualifications

Necessary qualifications:

Notwendige Qualifikation:

Master's / PhD degree in computer science, mathematics, or similarProfound knowledge in machine learning methods and computer vision.

Proven programming skills in Python, R and/or similar, e.g., evident through past projects or public source code on github.com. Unix shell scripting is also required.

Structured and independent working practice, excellent communication and English language skills.

Desirable qualifications:

Proven ML programming experience through previous projects and actively maintained, public github contributions.good publication track record, dependant on career stage, e.g., publications in CVPR, ECCV, ICCV, and affiliated workshops and/or IEEE TMI, PAMI, or similar Q1 journals (https://www.scimagojr.com/). Applicants with publications predominantly in potentially predatory or corrupted venues (https://beallslist.net/) will not be considered.depending on career ambitions: experience with teaching organisation or industrial collaborations.

Supplementary description

Candidates who lack the above-mentioned skills should have a strong motivation to develop these skills during their training. We are searching for candidates who are able to work independently and who have demonstrated excellent presentation and writing skills. The applicant will be expected to interact and collaborate with colleagues from different disciplines.During this employment you will have the opportunity to work with industry and highly esteemed international partners, amongst them research labs at Stanford, MIT, Imperial College London, King's College London and NYU. You will be able to visit them for research stays and discuss highly relevant topics also directly with other leading scientists.

We offer the opportunity to conduct cutting-edge research and advance the state-of-the-art, thereby contributing to improved machine learning methods. In addition, successful candidates will assist in the establishment of new research directions and can communicate these at international conferences and in scientific journals. We offer an inspiring working, an operational pension scheme, flexible working hours, mobile/remote working, compatibility of family and work and a research environment in a dynamic and growing research group with clinical, industrial, and academic cooperation partners in a city with a high quality of life and short commuting distances.

The final job classification is subject to conditions of Bavarian and German public service regulations.

Notice

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Release date: 17.07.2023