Starting on November 25th at 4pm with approximantely montly cadence 3 high-tech seminars will be offered by industrial and academic experts on radar sensor, tomographic reconstruction for high-resolution radar systems, Internet-of-Things key enabling technologies and solutions. Registration is free of charge at the webpage
https://infineon.avature.net/30minutestech
The third call for admission to the PhD programme (XXXVII cycle, 2021/2022) is now open. There are 12 positions available with full scholarships on research and innovation, and green thematics.
All interested PhD students are encouraged to enroll for free to the Brain-Inspired Computing Workshop 2021, held in Modena on October 7th and 8th 2021.
The workshop, now at its second edition after the well-received premiere in 2019, is based on invited contributions and aims at exploring different aspects of neuromorphic computing: from biologically realistic models for computation to synaptic and neuromorphic electronics to drive the latest generation of artificial neural networks. The main objective of this highly interdisciplinary initiative is to bring together different communities (e.g., electronic engineers, computer scientists, neuroscientists) and explore together areas of research apparently distant from one another such as experimental and computational neuroscience, electronics, and informatics maintaining a common thread.
Dal 12 luglio al 6 agosto è organizzata la terza edizione della Summer School del programma di cooperazione bilaterale Italia-Cina CI-LAM sui temi dell’Advanced Manufacturing.
Allego la brochure della scuola che si terrà in remoto su piattaforma CISCO WebEx e il Bando per avanzare la propria candidatura.
La Scuola è rivolta a studenti laureandi di Magistrale e di scuole di PhD di area Ingegneria dell’Informazione e Industriale.
Ulteriori informazioni possono essere reperite sul sito www.cilam.org oppure inviando una mail direttamente a me: breglio@unina.it
The aim of the course is to introduce students to two important elements of data engineering technology that makes it possible to extract valuable knowledge from “Big Data”, namely distributed data processing using the MapReduce framework, and data analytics of large graphs. In each case, the course will first introduce fundamental theoretical and architectural concepts, and then present technology for using MapReduce and querying large graphs, respectively. For MapReduce, we present examples of algorithms that can be successfully parallelised and thus are able to take advantage of distributed data architectures, and we will suggest practical exercises using the Spark and Hadoop technology stack. Regarding graph analytics, we focus on community detection algorithms as an example, and we will usethe Neo4J graph DBMS along with the Graph Data Science library for practical exercises.
The course will be held online in the ICT class of Microsoft Teams by professor Paolo Missier and Ph.D. Luca Gagliardelli
