Artificial Intelligence

Artois University; France; In loco; EUR 42.000 - 56.000; Tempo pieno

Descrizione del lavoro

Artois University seeks a First Stage Researcher (R1) for a temporary postdoc in the Computer Science field. The project focuses on interpreting internal mechanisms of AI models, linking internal weights and activations to human-interpretable concepts to enhance transparency and reliability.

The role requires a PhD in Computer Science, AI, or ML, with strong deep learning and explainable AI skills, and proficiency in Python and PyTorch.

Competenze

• PhD in Computer Science, AI, or ML is required.
• Strong skills in deep learning and explainable AI.
• Proficiency in Python and PyTorch or equivalent frameworks.

Mansioni

• Contribute to the task entitled “Interpreting the Internal Mechanisms of Models” by analysing AI models and their internal representations.
• Map and understand internal representations across modalities, disentangling latent concepts.
• Analyse learning dynamics and design architectures that promote performance and interpretability.
• Apply mechanistic understanding to reduce hallucinations in AI systems.

Formazione

PhD in Computer Science, AI, or Machine Learning

Strumenti

PyTorch

Descrizione del lavoro

Organisation/Company Artois University Department 62 Research Field Computer science » Informatics Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Application Deadline 5 Nov 2026 - 12:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 37.5 Offer Starting Date 1 Nov 2026 Is the job funded through the EU Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

As part of the research project, you will contribute to the task entitled “Interpreting the Internal Mechanisms of Models,” which aims to analyse and gain an in-depth understanding of how AI models work internally by establishing explicit connections between their internal structures, including weights, activations and gradients, and human-interpretable concepts. The objective is to move beyond purely behavioural interpretability and develop a mechanistic and causal understanding, which is essential to ensuring the transparency and reliability of AI systems. Your work will focus on mapping and understanding the internal representations of AI models by identifying alignments across modalities and disentangling latent concepts. You will also analyse learning dynamics and design architectures that promote both performance and interpretability. Finally, you will apply mechanistic understanding to advanced reasoning by making implicit reasoning processes understandable and reducing hallucinations.

Research Field Communication sciences » Science communication Education Level PhD or equivalent

Skills/Qualifications

PhD in Computer Science, AI, or Machine Learning. Strong skills in deep learning, explainable AI, Python, and PyTorch or equivalent frameworks.

Specific Requirements

AI, or Machine Learning. Strong skills in deep learning, explainable AI, Python, and PyTorch or equivalent frameworks.