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Pozicija u Trentu, Italija

Ako želite da radite istraživanje i razvoj na polju mašinskog učenja i data mining-a, ako volite skijanje i biciklizam,

otvorena je pozicija u Trentu, Italija.

Opis pozicije možete pronaći na sledećem linku https://euraxess.ec.europa.eu/jobs/194141

Informacije o osobi sa kojom biste radili i koja je odgovorna za projekat možete naći na sledećem linku https://scholar.google.com/citations?user=FY6F0McAAAAJ&hl=en

Institut „Mihajlo Pupin Automatika“

Institut „Mihajlo Pupin Automatika“ d.o.o traži mlade i ambiciozne saradnike, sa završenim elektrotehničkim fakultetom i poželjno master studijama.

Potrebno je više izvršilaca na poslovima programiranja, projektovanja, implementacije i održavanja sistema za naplatu putarine i kontrole pristupa.

Više informacija možete naći ovde.

Praksa IMP

Institut Mihajlo Pupin (http://www.pupin.rs/) nudi mogućnost prakse u trajanju od mesec dana za studenta Odseka za signale i sisteme. Praksa je u oblasti korišćenja Matlab Simulink programskog paketa za rad u realnom vremenu i obuhvata: analizu mogućnosti i ograničenja rada Simulinka u realnom vremenu, konfigurisanje slx modela (koji standardnim komunikacionim protokolom komunicira sa Atlas Max-RTL procesnim kontrolerima) za rad u realnom vremenu, izvšavanje modela u realnom vremenu u vidu nezavisne aplikacije, i slično. Kontakt osoba: Nebojša Radmilović (Ova adresa el. pošte je zaštićena od spambotova. Omogućite JavaScript da biste je videli.).

Zaposlenje - Vega Valjevo

Firma Vega (http://www.vegadoo.rs) traži inženjera automatike. Više informacija možete naći ovde.

Predavanje "Deep Learning from Sequential Data", S. Vučetić

Vreme: sreda 17. maj 2017. u 17h.

Mesto: sala 313.

O predavanju:

In this talk we will discuss the state of the art approaches for descriptive and predictive analysis of sequential data, such as text and event logs. A critical challenge in the analysis of sequential data is data representation, which refers to converting the raw data into a form that is suitable for machine learning algorithms. Many machine learning algorithms, such as neural networks, require the input to be provided as a fixed-length vector and, for a long time, this has been considered a major obstacle for successful learning from sequential data. The recent progress in machine learning has resulted in several powerful ideas for better representation and learning from sequential data. Among those ideas, probably the most powerful are distributed representations and deep learning. We will describe the intuition behind these ideas and demonstrate their promise by showing our recent results on the analysis of micro-blogging data and medical records data.

O predavaču:

Slobodan Vucetic is an Associate Professor and Chair of the Department for Computer and Information Sciences at Temple University. He got his Ph.D. degree in Electrical Engineering from Washington State University in 2001, and his B.S. and M.S. degrees in Electrical Engineering are from the University of Novi Sad. His research expertise and interests are in machine learning, data science, and big data. His research focuses on solving real-life knowledge discovery problems through development of novel machine learning algorithms and is driven by open problems in a wide array of disciplines such as Public Health, Medicine, Biology, Geosciences, Education, Marketing, Social Sciences, Traffic Engineering, and Industrial Engineering. Dr. Vucetic has published over 100 research papers that have been cited over 5,000 times and his current research is funded by the U.S. National Science Foundation (NSF), the National Institutes or Health, and industry. He is a recipient of the NSF CAREER award.

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