Doktorat iz elektrotehniških znanosti (Univerza v Ljubljani, Fakulteta za elektrotehniko)

Magisterij iz fizike (Univerza v Ljubljani, Fakulteta za matematiko in fiziko)

Diploma iz fizike (Univerza v Ljubljani, Fakulteta za matematiko in fiziko)

Docent (Univerza v Ljubljani, Fakulteta za elektrotehniko)

Asistent (Univerza v Ljubljani, Fakulteta za elektrotehniko)

Raziskovalec (Univerza v Ulmu, Nemčija)

Asistent (Univerza v Ljubljani, Fakulteta za elektrotehniko)

Gostujoči raziskovalec na Institute for Laser Technologies in Medicine and Metrology (Univerza v Ulmu, Nemčija)

Mladi raziskovalec v Laboratoriju za slikovne tehnologije (Univerza v Ljubljani, Fakulteta za elektrotehniko)

Ernesto Pini, Peter Naglič, Miran Bürmen, Alexander Gatto, Henrik Schäfer, Diederik S. Wiersma, Lorenzo Pattelli: Experimental determination of effective light transport properties in fully anisotropic media. Advanced Photonics Nexus, 3(5):056017, 2024 [doi:10.1117/1.APN.3.5.056017 ] [COBISS-ID: ]

Miran Bürmen, Franjo Pernuš, Peter Naglič: MCDataset: a public reference dataset of Monte Carlo simulated quantities for multilayered and voxelated tissues computed by massively parallel PyXOpto Python package. Journal of Biomedical Optics, 27(8):083012, 2022 [doi:10.1117/1.JBO.27.8.083012 ] [COBISS-ID:110538243 ]

Peter Naglič, Franjo Pernuš, Miran Bürmen: Reflectance calibration of multimode optical fiber probes by probe-to-target distance reflectance profile modeling. Measurement, 203:112002, 2022 [doi:10.1016/j.measurement.2022.112002 ] [COBISS-ID:126338051 ]

Peter Naglič, Yevhen Zelinskyi, Boštjan Likar, Miran Bürmen: Determination of refractive index, size, and solid content of monodisperse polystyrene microsphere suspensions for the characterization of optical phantoms. Biomedical Optics Express, 11(4):1901-1918, 2020 [doi:10.1364/BOE.387619 ] [COBISS-ID:12953940 ]

Yevhen Zelinskyi, Peter Naglič, Franjo, Pernuš, Boštjan Likar, Miran Bürmen: Fast and accurate Monte Carlo simulations of subdiffusive spatially resolved reflectance for a realistic optical fiber probe tip model aided by a deep neural network. Biomedical Optics Express, 11(7):3875-3889, 2020 [doi:10.1364/BOE.391163 ] [COBISS-ID:26951939 ]