\"HHPC - Horizon HPC\" - Bando CR 008 2026 ISTI PI

Italian Ministry of Education, University and Research; Italia; On-site; EUR 45,000 - 65,000; Full time

Job summary

CNR - ISTI in Italy seeks a researcher to develop and test methods for analysing mobility data, semantically enriching and summarising trajectories. The work will involve data mining, machine learning and AI to identify patterns and create compact trajectory representations, integrating GPS traces and contextual data.

Results will be disseminated via publications and international conferences. Candidates should have scientific experience evidenced by publications; the position emphasizes

Qualifications

• Publications in the field are valued.
• Experience with relevant datasets and analysis methods is desirable.
• Ability to communicate results in scientific venues.

Responsibilities

• Develop and test methods for analysing mobility data.
• Semantically enrich and summarize mobility data to extract knowledge.
• Integrate GPS traces, points of interest and contextual data to interpret spatio-temporal behaviour.
• Publish results and present at international conferences.

Skills

Data mining

Machine learning

Artificial intelligence

Semantic enrichment

Spatio-temporal analysis

Job description

Organisation/Company CNR - ISTI Research Field Computer science Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 9 Nov 2026 - 12:00 (UTC) Country Italy Type of Contract To be defined Job Status Not Applicable 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

The researcher will develop and test methods for analysing, semantically enriching and summarising mobility data to extract knowledge and interpret spatio-temporal behaviour. Activities will use data mining, machine learning and artificial intelligence to identify patterns and produce compact trajectory representations that preserve semantic information, integrating GPS traces, points of interest and contextual data. Experiments on real-world datasets and validation using appropriate metrics and statistical methods are planned. Scientific experience in the field, evidenced by relevant publications, will be valued. Results will be disseminated through scientific publications and participation in international conferences.

• Italy

Eligibility of fellows: country/ies of residence:

• Italy
• EUROPE
• OTHER
• E.U.

Eligibility of fellows: nationality/ies: