Novice

Uspešen zagovor magisterija Doroteje Novak

Nova članica programske skupine, Doroteja Novak, je v četrtek, 14.9.2017, uspešno zaključila študij farmacije. Z zagovorom naloge z naslovom »Načrtovanje in sinteza novih zaviralcev podenote A bakterijske DNA giraze« si je s pohvalo »cum laude« pridobila strokovni naziv magistra farmacije. Čestitamo!

Doroteja

Razpis za dve delovni mesti v sektorju radioterapije – oddelek radiofizika

Onkološki inštitut Ljubljana v sektorju radioterapije, oddelek radiofizike, razpisuje dve delovni mesti medicinski fizik – pripravnik. Za obe delovni mesti je zahtevana izobrazba VII/2, univ. dipl. fiz.

Delovni mesti sta razpisani za določen čas 12 mesecev.

Opis dela in nalog je sledeč: načrtovanje obsevanj, absolutna in relativna dozimetrija, zagotavljanje in preverjanje kakovosti, skrb za programsko in strojno dozimetrično opremo, odgovornost za strokovno delo in zakonitost ter ostala dela po nalogu vodje. Pripravnik bo delo opravljal pod nadzorom mentorja.

Več informacij najdete na spletni strani Zavoda za zaposlovanje.

Predavanje Ivana Štajduharja

IS_RI

V sredo, 10.5.2017, ob 16ih vljudno vabimo na predavanje izr. prof. Ivana Štajduharja (Department of Computer Engineering, University of Rijeka – Faculty of Engineering) z naslovom Artificial Intelligence in Medicine. Predavanje bo potekalo v predavalnici F2 v prvem nadstropju stavbe za fiziko, Jadranska 19, Ljubljana.

Artificial Intelligence in Medicine

Would it be possible to supersede physicians with computers, to some extent, at least? The idea of computer-aided diagnosis in diagnosing and treating illnesses has been around from nineteen-eighties. It was then that scientists discovered that, by applying statistical algorithms on real-world patient data, using so-called machine learning, one can establish useful (almost-)out-of-the-box mathematical models that perform well at describing some problems. In the last decade, significant increases occurred worldwide in the level of informatics-readiness of clinical centres, in the availability of standardised technology for data exchange and storage, and in the abundance of quality medical radiology techniques. This, in turn, resulted in an explosion in the availability of voluminous data, enabling further advances in the field of computer-aided diagnosis and treatment. In this lecture, in addition to some fundamentals related to the field, several topics concerning medical image analysis will be discussed: learning predictive models for diagnosing knee injury, data preprocessing for reducing model complexity and transfer learning in medical radiology domain (“RadiologyNet”, a research project conducted in collaboration with the Clinical Hospital Centre Rijeka, Croatia).