Machine Learning, Artificial Intelligence, Natural Language Process, Statistics
The research collaborator will work on the development of software prototypes aimed at improving the performance improvement of Uffici del Processo. The software solutions will process big data by means of statistical and machine learning techniques. The position is framed into an National initiative: "Progetto unitario per la diffusione dell'Ufficio per il Processo e l'implementazione di modelli operativi innovativi negli Uffici giudiziari per lo smaltimento dell'arretrato (UPPTF)ﬂ relatively to the so called Macroarea 3 ("Per una giustizia giusta: innovazione ed efficienza negli uffici giudiziari - GIUSTIZIA AGILE"), which involves the court of Firenze, Pisa, Lucca, Roma and Perugia. In particular, the activity research will focus on workflow management to tackle backlog material and sentence classification and retrieval. Moreover, the research collaborator might be involved on training activities for court staff involved in the project.
The research activity will focus on the development of software solutions aimed at improving the performance of Uffici del Processo. In particular, the research collaborator will apply statistical and machine learning solutions to deal with a large amount of data generated by the different courts involved in the projects. The aim of these software solutions is to reduce the backlog of the court offices and classify and efficiently retrieve court sentences. The research collaborator might be involved also on training activities for the court staff.
- A Master's Degree;
- Good knowledge of written and spoken English.
- Knowledge of a programming language;
- Knowledge of the most known machine learning algorithms and familiarity with the most advanced statistical techniques
“Per una giustizia giusta: Innovazione ed efficienza negli uffici giudiziari /Giustizia AGILE”, CUP J89J22000900005 ASSE I, Obiettivo Specifico 1.4, Azione 1.4.1 del Programma Operativo Nazionale Governance e Capacità Istituzionale 2014-2020 (P0240)
Apply ONLINE only.
Before filling in the application form, please read thoroughly the full call and collect all the files you may need:
- Personal info and contact info (compulsory);
- University degree (compulsory);
- PhD (compulsory only if stated in the full call).
- The scanned copy of a valid identity document (Passport or Identity Card - compulsory);
- Your CV in English (compulsory).