Organisation/Company: Polytechnic University of Bari
Department: Dipartimento di Ingegneria Elettrica e dell’Informazione
Research Field: Other
Researcher Profile: Recognised Researcher (R2)
Country: Italy
Application Deadline: 20 Mar 2025 - 23:59 (Europe/Rome)
Is the job funded through the EU Research Framework Programme? European Union / Next Generation EU
Offer Description
Project Name: Artificial Intelligence techniques and technologies to support data analysis in complex contexts
Project Idea: The researcher will be involved in studying and implementing Artificial Intelligence techniques devoted to automatically classify a context and manage outputs of complex systems replying to structured and non-structured inputs. Reference scenarios are referred to mobile unpredictable environments.
Research Commitment Details: The research must be directed to produce novel methods and algorithms to design, implement and adopt reliable advanced information systems. Research results will be published in esteemed international journals and conference proceedings.
Teaching Commitment Details: The researcher will be required to teach no less than 6 CFU. Subjects will belong to the SSD IINF-05/A.
Teaching Activities: The selected researcher will be involved in teaching activities on subjects related to the Information Systems field in Undergraduate, Master of Science, and PhD courses within the framework of the Department course plan.
Proven Experience Required: Experience in the design of distributed information systems; study of techniques and technologies of Artificial Intelligence aimed at concrete applications in complex contexts (Industry 4.0, healthcare, building automation, Distributed Ledger Technologies, and automotive).
Required IT Skills: Distributed Ledger Technologies, Formal Argumentation, algorithms and data structures, programming languages (compiled and interpreted, particularly for logic programming), (m-)DBMS (relational and non-relational), KBMS, automatic reasoning software systems, advanced operating systems.
Knowledge Level: Excellent written/listening comprehension and spoken English; excellent written technical English. The evaluation will be based on the candidate's scientific publications in English. If needed, the evaluation committee can verify English language knowledge through an oral examination.
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