Assistant Professor at University of Bonn

contact: f.bernardpi::gmail::com

**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.

Currently we offer two fully-funded PhD positions:

- PhD position in the area of
**Deep Multi-Modal Image Synthesis for COVID-19 Imaging and Beyond**. Candidates should have a solid theoretical understanding of machine learning techniques and hands-on experience in training deep neural networks. Experience with medical image data is not essential but a plus. - PhD position in the area of
**Algorithmic Visual Computing for 3D Shape Analysis**. Candidates should have a keen interest in the utilisation of mathematical optimisation and/or graph-theoretic algorithms to push the current state of the art in visual computing. A solid background in mathematics is required, knowledge of visual computing is not essential but a plus.

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:

- visual computing (computer vision, computer graphics, geometry processing, shape analysis)
- mathematical optimisation (convex, non-convex, discrete, continuous)
- machine learning (deep learning)

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

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

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:
```