Florian Bernard

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Assistant Professor at University of Bonn

contact: f.bernardpi::gmail::com

News

10/2021: I joined the Department of Visual Computing at University of Bonn as Assistant Professor and head of the ‘Learning and Optimisation for Visual Computing’ group.

09/2021: Our paper Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation has been accepted at NeurIPS 2021.

05/2021: I received a CVPR 2021 Outstanding Reviewer Award.

03/2021: Two submissions were accepted and selected for oral presentation at CVPR 2021:
o Isometric Multi-Shape Matching
o i3DMM: Deep Implicit 3D Morphable Model of Human Heads

10/2020: Two ACM Transactions on Graphics (TOG) papers, to be presented at SIGGRAPH Asia, are now available:
o PIE: Portrait Image Embedding for Semantic Control
o RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video

04/2020: I joined the Chair of Computer Vision & Artificial Intelligence at TU Munich as Visiting Professor.

News archive

Useful resources

PhD positions

Currently we offer two fully-funded PhD positions:

PhD students will be part of the Learning and Optimisation for Visual Computing Group at the University of Bonn. It is expected that candidates are passionate about research and want to push the boundaries of current visual computing solutions. Successful applicants typically have a strong background and solid working knowledge in basic mathematics (linear algebra, analysis) and computer science (algorithms, data structures), and some experience in at least one of the following areas:

We accept applications until the positions have been filled. Note that we do reply to unspecific/generic applications.

Self-funded PhD students

We also accept self-funded PhD students (e.g. through scholarships, etc.). Please get in touch to discuss potential research directions.

Self-assessment

Your application should include a self-assessment in which you assign grades from 1-5 (1 means best) to indicate your background in relevant topics. In case you get selected for an interview it will be based on your self-assessment.

Since I am not familiar with all international grading systems it is recommended that international applicants provide a short summary on the ranking of their university (e.g. ‘Top 10% in India’), and also rank their total grade (e.g. ‘Top 20% of students across all computer science graduates at the university’).

You can use the following template (feel free to leave topics with no experience blank).

linear algebra: 
analysis & calculus: 
algorithms & data structures:
computer vision: 
computer graphics: 
geometry processing/shape analysis: 
mathematical optimisation: 
deep learning: 

ranking of my university:
ranking of my performance: