Contract type : Fixed-term contract
Level of qualifications required : PhD or equivalent
Fonction : Post-Doctoral Research Visit
Level of experience : From 3 to 5 years
Context
The position is open in the context of the joint project between Airbus R C& T, Cerfacs and Inria.
In the context of supervised learning (e.g., learning regression models as approximation of functions) or unsupervised learning (probability distributions learning), we face CPU time issues as the dimension grows.
We encounter these problems of supervised learning in high dimension when we are interested in the prediction of physical quantities which are very often spatial fields. As an example, we can mention the learning of wall laws in a fluid calculation which allows us not to refine too much near a wall.
Assignment
For both continuous graphical models and deep tensor trees, two main operations are particularly costly:
- Algebraic operations on tensors;
- The exploration and quantization of many tree or graphical model configurations (Directed Acyclic Graphs).
In the case of tensor networks, learning the coefficients in the leaves of the tree is also expensive (alternate least-squares).
Main activities
The objective of this post-doc is to accelerate these three functionalities by using different strategies.
Concerning the tensor algebra, we will allow ourselves not to make exact calculations if the improvement is important and the error is controlled.
For the exploration, we can set up strategies of exploration by neighborhoods which implement calculations by batches distributed on the various resources.
Regarding the learning of the coefficients, while the alternate least squares algorithm is a well-stablished method, new algorithms have been developed as well as new tensor formats. It is expected to implement and test these algorithms.
The Celeste library is a C++ library for tensor computation based on the Tucker and Tensor Train formats. Our objective from these applications is to validate and improve the performance of our methods and to integrate it into the netwkork tensor and the graphical models
Benefits package
- Subsidized meals
- Partial reimbursement of public transport costs
- Possibility of teleworking and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage
Remuneration
2653€ / month (before taxs)
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General Information
- Theme/Domain :
Distributed and High Performance Computing
Scientific computing (BAP E) - Town/city : Talence
- Inria Center : CRI Bordeaux - Sud-Ouest
- Starting date : 2022-10-01
- Duration of contract : 2 years
- Deadline to apply : 2022-07-15
Contacts
- Inria Team : HIEPACS
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Recruiter :
Coulaud Olivier / Olivier.Coulaud@inria.fr
The keys to success
This position is intended for candidates with a strong background in computational sciences, preferably holding a PhD in applied mathematics, with some knowledge/experience in machine learning, probability, numerical linear algebra.
About Inria
Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.
Instruction to apply
Thank you to send:
- CV
- Cover letter
- Support letters (mandatory)
- List of publication
Defence Security :
This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.
Recruitment Policy :
As part of its diversity policy, all Inria positions are accessible to people with disabilities.
Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed.