Merge branch 'internships/internship-ml4eva-2023' into 'master'

[jobs] Add internship position on ml4eva.

See merge request !166

_jobs/2022-11-04-internship-ml4eva.md

+---
+layout: job
+title: Internship Position at CEA LIST - LSL
+short_title: Internship Position
+short: Deep Learning for improving formal verification with Frama-C / Eva
+date: 04-11-2022
+filled: false
+keywords: Deep Learning, Graph Neural Networks , Representation Learning, Static analysis, Formal methods
+---
+
+# {{ page.short }}
+
+
+Institution
+-----------
+
+The French [Alternative Energies and Atomic Energy Commission](https://www.cea.fr/) (CEA) is a key
+player in research, development, and innovation.  Drawing on the widely
+acknowledged expertise gained by its 16,000 staff spanned over 9 research
+centers with a budget of 4.1 billion Euros, CEA actively participates in more
+than 400 European collaborative projects with a large number of academic
+(notably as a member of [Paris-Saclay University](https://www.universite-paris-saclay.fr))
+and industrial partners. Within the CEA Technological Research Division, the
+[CEA List](https://www-list.cea.fr) institute addresses the challenges coming from smart digital systems.
+
+Among other activities, CEA List's Software Safety and Security
+Laboratory (LSL) research teams design and implement automated
+analysis in order to make software systems more trustworthy, to
+exhaustively detect their vulnerabilities, to guarantee conformity to
+their specifications, and to accelerate their certification.  In
+particular, the [Frama-C platform](https://frama-c.com/) is dedicated to perform
+a wide range of analyses over C programs (with an experimental C++ front-end).
+
+
+Objectives
+----------
+
+Inside Frama-C, the Eva plugin provides several static analysis techniques
+dedicated to the automatic inference of program properties and, in particular,
+runtime errors (arithmetic overflows, invalid memory accesses and other C
+undefined behaviors). These techniques are efficiently combined, but their
+activation can be expensive in terms of resource consumption (time and memory).
+It is therefore necessary to choose wisely which techniques to use in which
+case.
+
+These choices are now made by the analyst, who uses the tool to perform safety
+proofs on a software. They require a detailed knowledge of of Frama-C and its
+analysis techniques, both of which evolve with time. Moreover, to be as fine as
+possible, these choices can be made at the function or loop level in a sometimes
+large source code. This double difficulty, based on both the quantity of
+knowledge and the time needed, can be a blocking obstacle.
+
+To remove this obstacle, we have implemented promising machine-learning
+techniques. This involves treating the difficulty no longer as a problem of
+understanding the analysis tool but as a problem of understanding the relation
+between code patterns and the different analysis techniques. This has two
+advantages. On the one hand, the learning can be repeated as the tool evolves,
+in order to to remain adapted to the characteristics of the latest available
+techniques. On the other hand, the automatic choice of techniques can be done at
+a much finer level level than what a human can accomplish in a reasonable time.
+
+The objectives of the internship are to contribute to our machine-learning
+toolchain specialized on source code, to participate in its integration in
+Frama-C, and to improve its performance in the automatic selection of analysis
+strategies. Several internship directions are possible:
+
+- Investigate and develop specialized learning techniques on graphs (in
+  particular, Graph Neural Networks),
+- Explore learning techniques in the presence of unbalanced data
+- Study and develop a distributed version of the toolchain capable of fully
+  leveraging the resources of our cluster FactoryIA.
+
+All positions include theoritical research as well as prototyping and
+experimental evaluation.
+
+
+Qualifications
+--------------
+
+- **Minimal**
+
+  - Final-year master student in Computer Science or Computer Enginnering
+  - Solid ability in Python programming
+  - Solid ability with deep learning frameworks (TensorFlow or PyTorch)
+  - ability to work in a team
+
+- **Preferred**
+
+  - A certain taste for mathematical matters
+  - Some knowledge of functional programming, preferably OCaml
+  - Some knowledge of imperative programming, preferably C
+
+
+Characteristics
+---------------
+
+- **Duration:** 5-6 months
+- **Location:** [CEA Nano-INNOV](https://www.openstreetmap.org/#map=19/48.71238/2.19335), Paris-Saclay Campus, France
+
+- **Compensation:**
+
+    - €700 to €1300 monthly stipend (determined by CEA compensation grids)
+    - maximum €229 housing and travel expense monthly allowance (in case a relocation is needed)
+    - CEA buses in Paris region and 75% refund of transit pass
+    - subsidized lunches
+
+## Application
+
+If you are interested in this internship, please send to the [contact
+persons](#contact-persons) an application providing the following information:
+
+- Your resume;
+- A cover letter indicating how your curriculum and experience match the
+  qualifications expected and how you would plan to contribute to the project
+- Your bachelor and master transcripts
+- The contact details of two persons (at least one academic) who can be
+  contacted to provide references.
+
+Applications are welcomed until the position is filled. Please note that the
+administrative processing may take up to **3 months**, so we encouraged
+interested students to take contact as soon as possible.
+
+## Contact persons
+
+For further information or details about the internship before applying, please contact:
+
+- Michele Alberti (<michele.alberti@cea.fr>)
+- Valentin Perrelle (<valentin.perrelle@cea.fr>)