Post-doc
Title: Postdoc in 3D Computer Vision / Machine Learning
Location: Ecole Polytechnique, France
Start date: December 2021 - March 2022
Description:
A postdoctoral researcher position is available
at the LIX research laboratory of Ecole Polytechnique in France, in
the area of:
"Analyzing Large Scale 3D Shape Collections"
The goal of this project is to develop tools for the analysis of
large-scale 3D shape collections, by designing novel Machine Learning
techniques capable of processing and manipulating geometric data.
The ultimate goal is to design tools for shape labeling, recognition,
and matching across geometric data in 3D.
The ideal candidates should have strong background in at least some of
the following:
- Computer Vision and Image Processing
- Machine Learning
- Programming experience, especially in multimedia data analysis
- Geometry Processing and 3D shape analysis
Candidates must have a PhD at the starting time of the post-doc, and
should have publications in top venues in either Computer Vision
(CVPR, ICCV, etc.) or Computer Graphics (e.g., SIGGRAPH, Eurographics,
SGP). The expected duration of the post-doc is 2 years.
Venue and supervision:
Successful candidates will be based at Ecole Polytechnique located in
Palaiseau, Paris area, France. The project will be supervised by
Maks
Ovsjanikov (LIX, Ecole Polytechnique).
How to apply:
To apply, please contact Maks Ovsjanikov
(maks@lix.polytechnique.fr) and include
[Computer Vision post-doc application]
in the subject line.
In your email, please include: 1) your CV
with a publication list and 2) a short cover letter. In your cover letter,
please
make sure to explicitly describe your experience in the 4 topics mentioned above.
Reference letters are optional but may need to be provided upon request.
Post-doc
Title: Postdoc in 3D Geometry Processing / Computer Graphics
Location: Ecole Polytechnique, France
Start date: December 2021 - March 2022
Description:
A postdoctoral researcher position is available
at the LIX research laboratory of Ecole Polytechnique in France, in
the area of:
"Exploring Relations in Structured Data with Functional Maps"
The goal of this project is to develop tools for the analysis and
processing of complex geometric data, such as large-scale 3D model
collections, by building on techniques from Computer Graphics,
Geometry Processing and Machine Learning. The ultimate objective is to
create a unified framework for representing and analyzing structured
data to solve problems such as: shape comparison and matching, shape
segmentation and labeling, shape deformation and recognition among
others. The project will be funded, in part, by the ERC Starting Grant
EXPROTEA.
The ideal candidates should have strong background in at least some of
the following:
- Geometry Processing and 3D shape analysis
- Computer Graphics
- Knowledge of Differential Geometry
- Numerical Optimization and Machine Learning
- Programming experience, especially in multimedia data analysis
Candidates must have a PhD at the starting time of the post-doc, and should have publications in top venues in
either Computer Graphics or Geometry Processing (e.g., SIGGRAPH, Eurographics, SGP). The expected duration of the
post-doc is 2 years.
Venue and supervision:
Successful candidates will be based at Ecole Polytechnique located in
Palaiseau, Paris area, France. The project will be supervised by
Maks
Ovsjanikov (LIX, Ecole Polytechnique).
How to apply:
To apply, please contact Maks Ovsjanikov
(maks@lix.polytechnique.fr) and include
[Geometry Processing post-doc application]
in the subject line.
In your email, please include: 1) your CV
with a publication list and 2) a short cover letter. In your cover letter,
please
make sure to explicitly describe your experience in the 5 topics mentioned above.
Reference letters are optional but may need to be provided upon request.
PhD student
Title: PhD student position in 3D Geometry Processing / Computer Graphics / Computer Vision
Location: Ecole Polytechnique, France
Start date: December 2021 - March 2022
Description:
One PhD student position is available
at the LIX research laboratory of Ecole Polytechnique in France, in
the area of:
"Exploring Relations in Structured Data with Functional Maps"
The goal of this project is to develop tools for the analysis and
processing of complex geometric data, such as large-scale 3D model
collections, by building on techniques from Computer Graphics,
Geometry Processing and Machine Learning. The ultimate objective is to
create a unified framework for representing and analyzing structured
data to solve problems such as: shape comparison and matching, shape
segmentation and labeling, shape deformation and recognition among
others. The project will be funded, in part, by the
ERC Starting Grant
EXPROTEA.
The ideal candidates should have strong background in at least some of
the following:
- Numerical optimization (e.g., continuous optimization with quasi-Newton methods, optimality conditions,
constrained/unconstrained optimization, convex optimization, etc.)
- Numerical linear algebra (factorization methods for linear systems, sparse vs. dense systems, iterative vs. direct
solvers, etc.)
- Differential Geometry (notions of Riemannian manifold, tangent space, vector fields, etc.)
- Fourier Analysis and spectral methods (Laplace operators on surfaces and graphs, heat equation, wave equation, etc.)
- Multimedia data analysis (images, 3D shapes, volumetric data) including learning-based techniques.
- Programming experience, especially in multimedia data analysis
For the PhD position, the candidate should have a Master's degree with
preference given to those having strong research experience. The
expected duration of the PhD is 3 years.
Venue and supervision:
Successful candidates will be based at Ecole Polytechnique located in
Palaiseau, Paris area, France. The project will be supervised by
Maks
Ovsjanikov (LIX, Ecole Polytechnique).
How to apply:
To apply, please contact Maks Ovsjanikov
(maks@lix.polytechnique.fr) and include
[PhD application]
in the subject line.
In your email, please include: 1) Your CV,
2) Transcripts (list of courses taken with notes, for both Bachelor's and Master's-level courses),
3) Recommendation letters, if you have them, or, if not, contact information your previous teachers or supervisors that
can provide feedback about your work, 4) A short cover letter. In your cover letter,
please
make sure to explicitly describe your experience in the 6 topics mentioned above.
Reference letters are optional but may need to be provided upon request.
Internships
Note: Unfortunately, I do not accept undergraduate internship requests. My only internship offers are for
students currently enrolled in, or having completed their Master's degrees. I am sorry for not being able
to respond to each individual undergraduate internship request.
Position: Research internship (Master's or PhD student)
Title: Deep Learning for 3D shape Matching
Location: Ecole Polytechnique, France
Start date: March — April 2020
Description:
The goal of this project is to develop a method for finding correspondences between
non-rigid 3D models by using techniques from Machine Learning (Deep Learning in
particular), Geometry Processing and Computer Graphics. Given a collection of 3D
models, such as scans of humans in different poses, the goal is to learn effective
ways for establishing point or region correspondences between new unseen pairs.
The ideal candidates should have strong background in
at least some of the following:
- Numerical optimization and Numerical linear algebra
- Differential Geometry
- Fourier Analysis and spectral methods
- Machine Learning
- Programming experience, especially in multimedia data analysis (images, 3D shapes, volumetric data)
Strong preference will be given to candidates who wish to pursue a PhD at the end of the
internship (starting in September 2020). The PhD has funding guaranteed by the ERC Starting
Grant EXPROTEA.
Successful candidates will be based at Ecole Polytechnique located in Palaiseau,
Paris area, France. The project will be supervised by Maks Ovsjanikov.
How to apply:
To apply, please contact Maks Ovsjanikov: maks@lix.polytechnique.fr, and put
[internship application] in the subject line. In your email, please include your CV,
the transcript of courses taken with the grades and a short cover letter. In your
cover letter, please describe how your experience relates to the topics in this
announcement.