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PhD Kick-Off Meeting for the XL Cycle

The PhD Kick-Off Meeting will be held on Tuesday, November 5, 2024, at the Enzo Ferrari Engineering Department. This event will welcome new PhD students and provide essential information across our PhD programs: Information and Communication Technologies (ICT), Mechanical and Vehicle Engineering (MV-EF), and Civil, Environmental, and Materials Engineering (CAM).
The program begins with a plenary session including department introductions, safety and identity briefings, Erasmus opportunities, and administrative instructions, followed by parallel sessions specific to each PhD program. Attendance in person is highly encouraged, with remote participation available by arrangement with the PhD Course Coordinator.

Location: MO-25 building, "Enzo Ferrari" Engineering Department, with links to Teams for remote access.

PhD Course: Privacy Preserving Computation Methods

From July 22nd through 24th, in the meeting room at the first floor of MO27 building (DIEF), Prof. Julián Salas Piñón will lecture the course on "Privacy preserving computation methods: from the basics to Privacy Preserving Machine Learning".

This is a regular course of the PhD school and attendance is warmly recommended.

Passing the final exam will award you 3 CFD.

We kindly ask to fill the form if you are interested in participating.

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23 Exciting Opportunities in ICT at the Department of Engineering "Enzo Ferrari" - University of Modena and Reggio Emilia - UNIMORE (Italy) - Deadline: July 26, 2024 1:00 PM (CET)!

We are looking for motivated candidates willing to undertake challenging but rewarding research projects. The minimum duration of the course is three years. Details on the 23 available positions and admission requirements are attached as included in the full unimore call available at https://www.bandi.unimore.it/AZdoc/CallPhDENGFinale.pdf. The deadline for applications is 26/07/2024, 1:00 PM (CET).

Events Event archive

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Prof. Kowalski Leads Course on Explainable AI in Neural Networks - Now Available via Livestream

From November 7th to 14th, Prof. Piotr Andrzej Kowalski will be teaching an advanced course on Explainable Artificial Intelligence (XAI) with a focus on neural networks. The course, aimed at shedding light on the decision-making processes of complex neural models, addresses one of AI's biggest challenges: its "black-box" nature.

Throughout the four sessions, participants will learn essential interpretability techniques such as LIME and SHAP, explore sensitivity analysis methods, and delve into real-world applications in healthcare and environmental monitoring. A strong emphasis will also be placed on ethical issues, including bias and fairness in AI.

For those unable to attend in person, the course is also available via livestream. We kindly ask to fill the form if you are interested in participating.

PhD course: Natural Language Interfaces to Data

From October 15 to 17, in the meeting room at the first floor of the MO27 building (DIEF), Prof. Georgia Koutrika (Athena Research Center) will lecture the course on "Natural Language Interfaces to Data".

The course encompasses 12 hours and is composed of three lectures (from 9am to 1pm). Online attendance is also possible for external audience.

This is a regular course of the PhD school and attendance is warmly recommended. Passing the final exam will award you 3 CFD.

We kindly ask to fill the form if you are interested in participating.

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ICT Summer Camp: Enhance your Soft Skills

Monastero di S. Domenico di Montecreto, 26-30 August, 2024

  • Duration: 5 days and 4 nights
  • Check-in: Monday, from 2 PM to 3 PM
  • Check-out: Friday, at 2:30 PM
  • Accommodation: in double or triple room

Registrations are open for the first edition of the PhD Summer Camp organized by our doctoral course. The program and the link for registrations at the address below

Testimonies from our studentsRead all

Francesco Montorsi
Software Engineer, Empirix Italy Spa
My current role: Development of high-performance systems for the analysis of 2G/3G/4G/5G mobile core networks
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Thanks to my PhD I exercised my ability to analyze complex statistical problems, propose a model for them and then possible solutions.
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