PhD: « Sustainability and Explainability through Learning on Large Knowledge Graphs » – 36-months contract
11 Mar 2024
Job Information
- Organisation/Company
Ecole Nationale Supérieure des Mines de Saint Etienne- Research Field
Other- Researcher Profile
First Stage Researcher (R1)- Country
France- Application Deadline
5 May 2024 - 12:00 (Africa/Abidjan)- Type of Contract
Temporary- Job Status
Full-time- Hours Per Week
40- Offer Starting Date
1 Oct 2024- Is the job funded through the EU Research Framework Programme?
Not funded by an EU programme- Is the Job related to staff position within a Research Infrastructure?
No
Offer Description
Context:
The Fayol institute of Mines Saint-Étienne and the Laboratory of Informatics, Modeling and System Optimization (LIMOS, UMR 6158) is opening a PhD position in knowledge representation and machine learning, to work at the intersection of digital technologies and sustainable development.
The PhD proposal aims to find alternatives to Large Language Models (LLMs), characterized by a high number of trainable parameters and/or a high number of tokens in their training corpus. Using LLMs thus comes with high energy costs, in both training and inference phases, and a lack of transparency on the generated content. The objective of the PhD is to show that Knowledge Graphs such as Dbpedia, BabelNet or ConceptNet can be a solution to both problems. They have already been used in question answering tasks, despite their notable incompleteness on naive physics (basic spatial-temporal reasoning). The incompleteness of a Knowledge Graph can however be addressed by learning vector representations of the main concepts of the graph (its foundational ontology), whose geometric properties remain semantically interpretable.
Mission:
The objective of the PhD is to develop a method to train vector representations of concepts at low cost and produce a pre-trained language model from DBpedia (or similar). The pre-trained model would then be used for spatial-temporal reasoning in an industrial application, to prove the usefulness of such representations.
Activities:
Research will be conducted through bibliography management, experiment design and execution (on compute clusters), writing and presentation of results. During the PhD, the candidate will have to manage large volumes of data, use compute resources on dedicated CPUs/GPUs and develop code using machine learning libraries such as PyTorch. They will also have to apply lifecycle assessment methods to measure the environmental impact of a computation.
The PhD candidate may also help organize doctoral workshops or summer schools (with invited researchers) and attend the numerous scientific events being organized among members the Fayol institute or LIMOS.
Requirements
- Research Field
- Other
- Education Level
- Master Degree or equivalent
Skills/Qualifications
Technical skills and curriculum:
- Master’s degree or equivalent, in the domains of computer science, data science or applied mathematics
- Prior knowledge in:
- machine learning and/or natual langage processing
- formal logics and/org Semantic Web
- (large) relational databases and/or graph databases
Other skills:
- written and spoken English (writing of technical reports and oral presentations)
- pratical problem solving
- ability to generalize and formalize (mathematically)
- autonomy, initiative and intellectual curiosity
- Languages
- ENGLISH
- Level
- Good
- Languages
- FRENCH
- Level
- Good
Additional Information
- Website for additional job details
https://institutminestelecom.recruitee.com/o/doctorant-ou-doctorante-soutenabil…
Work Location(s)
- Number of offers available
- 1
- Company/Institute
- MINES SAINT-ETIENNE
- Country
- France
- State/Province
- FRANCE
- City
- ST ETIENNE
- Postal Code
- 42000
- Street
- 158 cours Fauriel
- Geofield
Where to apply
- Website
https://institutminestelecom.recruitee.com/o/doctorant-ou-doctorante-soutenabil…
Contact
- City
Saint-Étienne- Website
http://www.emse.fr/- Street
158 cours Fauriel , CS 62362- Postal Code
42023
STATUS: EXPIRED