INFORMATION PROCESSING SYSTEMS

UNIVERSITA' TELEMATICA SAN RAFFAELE ROMA; Italia; In loco; EUR 40.000 - 60.000; Tempo pieno

Descrizione del lavoro

UNIVERSITA' TELEMATICA SAN RAFFAELE ROMA invites applications for a temporary research position in Computer Engineering with a focus on Artificial Intelligence. The project covers Knowledge Representation, Information Visualization, and Explainable AI, integrating ML, DL, data mining, and NLP where appropriate.

Teaching activities will align with AI and computer engineering in the curriculum. The role emphasizes rigorous research, collaboration across disciplines, and contribution to both

Competenze

• Research in AI with ML/DL, NLP, data mining.
• Teaching AI and computer engineering topics in the curriculum.

Mansioni

• Conduct scientific and educational activities in AI and related fields.
• Develop and apply methods in knowledge representation, visualization, and XAI.
• Contribute to teaching within the department and university program.

Conoscenze

Artificial Intelligence

Machine Learning

Explainable AI

Knowledge Graphs

Data Visualization

Descrizione del lavoro

Organisation/Company UNIVERSITA' TELEMATICA SAN RAFFAELE ROMA Department DIPARTIMENTO DI PROMOZIONE DELLE SCIENZE UMANE E DELLA QUALITA' DELLA VITA Research Field Engineering Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 3 Oct 2026 - 23:59 (UTC) Country Italy Type of Contract Temporary Job Status Other Offer Starting Date 1 Jan 1970 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Scientific and educational activities will be conducted in the field of Computer Engineering, with a specific focus on Artificial Intelligence and its multidisciplinary applications. Research will concentrate on three key areas: Knowledge Representation (ontologies and knowledge graphs), Information Visualization for visual data analysis, and Explainable AI (XAI) to ensure the transparency and verifiability of intelligent systems. These methodologies may be integrated with techniques such as machine learning, deep learning, data mining, and natural language processing to develop complex, reliable decision-support systems. Teaching activities will align with these themes, covering courses in AI and computer engineering included in the University's academic curriculum.