Open call for 1 Postdoc & 1 PhD position on Machine Learning for Software Engineering (joint supervision with CEA List)
CEA List and Open University of Catalonia seek applications for a postdoc and a PhD position in the application of machine learning to (model-driven) system and software engineering. The selected candidates will be co-supervised between DR. Sébastien Gerard (Head of the LISE labs at CEA LIST, Paris) and Prof. Jordi Cabot (head of the SOM Lab, UOC, Barcelona). The candidate will be hosted by CEA in Paris but will carry out regular visits to Barcelona. PhD Funding is for a 3 years period. The postdoc position is for one year (with the possibility of extending the contract afterward).
The topic of the work will be the Cognification of model-driven engineering. Cognification is the application of knowledge (inferred from large volumes of information using machine learning and other artificial intelligence techniques) to boost the performance and impact of an engineering process. The work will evaluate and adapt cognification to Model-Driven Engineering (and Systems and Software Engineering in general) as a way to improve current software development processes.
One way would be the creation of smart bots to assist the engineers. A first example could be the creation of modeling bot playing the role of virtual modeling assistant able to suggest modeling improvements based on its knowledge of previous models for the same domain, ontologies or learned modeling best practices. A second example could be a process bot (e.g., an ISO26262 bot) that will assist engineers to design complex systems conforming certification processes such as ISO26262 in the automotive system domain. See other examples.
Concrete implementation of the results will take place in the context of the Papyrus open-source project.
For the PhD position, only highly qualified PhD candidates with a master’s degree in computer science should apply. For postdocs, a proven research track record in the related fields is expected. For both positions, the required skills include excellent knowledge in the field of software engineering, good programming skills and knowledge of machine learning techniques.
The application must contain:
- a motivation letter including a statement of your research interests
- a full curriculum vitae, including list of publications if any
- a minimum of two contacts that can provide letters of recommendation
Applications not complying with the above requirements will not be considered.
Positions are to be filled as soon as possible.
Open PhD Position: Blending intuition with reasoning – Deep learning augmented with algorithmic logic and abstraction
Within machine learning, deep learning, based on neural networks, is a subfield that has gained much traction since several high-profile success stories. Unlike classical computer reasoning, the statistical method by which a neural network solves a problem can be seen as a very primitive form of intuition, as opposite to classical computer reasoning. However, so far the only real success of deep learning has been its ability to self-tune its geometric logic that lets it transform data represented as points in n-dimension, to data represented as points in m-dimension, if we provide enough training data. Unlike a human being, a neural network does not have the ability to reason
through algorithmic logic. Furthermore, although neural networks are tremendously powerful for a given task, since they have no ability to achieve global generalization, any deviation in the input data may give unpredicted results, which limits their reusability. Considering the significant cost associated with neural network development, integrating such systems is not always economically viable. It is therefore necessary to abstract, encapsulate, reuse and compose neural networks.
Although lacking in deep learning, algorithmic logic and abstraction are today innate to classical software engineering, through programming primitives, software architecture paradigms, and mature methodological patterns like Model-Driven Engineering. Therefore, in this thesis, we propose to blend reusable algorithmic intelligence, providing the ability to reason, with reusable geometric
intelligence, providing the ability of intuition. To achieve such an objective, we can explore some ideas like integrating programming control primitives in neural networks, applying software architecture paradigms in neural networks models, and assembling modular systems using libraries containing both algorithmic modules and geometric modules.
The results of this thesis will be a stepping stone towards helping companies assemble AI systems for their specific problems, by limiting the costs in expertise, effort, time, and data necessary to integrate neural networks.
More info: thesis_deep_learning_intuition_reasoning
Contact: Shuai Li