Cilj projekta je istražiti mogućnosti primjene vještačke inteligencije (AI) u nastavi fizike radi unapređenja kvaliteta učenja, povećanja motivacije učenika i razvoja STEM kompetencija. Projekat podrazumijeva korištenje AI alata za generisanje zadataka, objašnjavanje fizičkih pojava, analizu učeničkih grešaka i personalizaciju nastavnog procesa.

Od otkrića cjelobrojnog kvantnog Hall-ovog efekta 1980. godine, topološke faze materije postale su jedno od najistraživanijih oblasti u modernoj fizici čvrstog stanja. Ovi mezoskopski sistemi se mogu jednostavno opisati topološkom invarijantom - fizikalnom veličinom koja se može izračunati iz sopstvenih stanja Hamiltonijana koji opisuje sistem u impulsnom prostoru. Topološka invarijanta uzima cjelobrojne vrijednosti i odgovorna je za postojanje vezanih stanja na granicama kvantnog sistema.

Rješavanje Poissonove jednačine i njene modifikacije u galaktičkim okolnostima Duraković Amel i Mistele Tobias Observatoire astronomique de Strasbourg, Institute of Physics of the Czech Academy of Sciences Case Western Reserve University

This project aims to study the rotation and total magnetization (as a function of time) of spherical, cubic, and disk-shaped, multidomain magnetic colloids in oscillating magnetic fields. To simulate the internal magnetic structure and dynamics of the colloids, we will take a multiscale approach consisting of a single-spin Landau-Lifshitz-Gilbert equation model [3], and a raspberry colloid model where the domains are thermal Stoner-Wohlfarth particles [4]. The knowledge gained in contrasting these approaches to “ground-truth” micromagnetics, using varying levels of complexity, will circumvent current limitations and help expand the potential applications of magnetic fluids in cancer treatment.

Magnetic Colloidal Polymers (MCPs) are polymer-like mesostructures composed of permanently crosslinked magnetic nanoparticles (MNPs), encompassing systems with varied magnetic content, solvent affinity, and composition profiles. Similarly to chemical polymers, their self-assembly into superstructures can be controlled through solvent selectivity and backbone composition. The interplay of solvophilic, solvophobic, and magnetic interactions gives rise to complex self-assembly behaviour that can be manipulated with external magnetic fields, offering dynamic control and high spatial resolution [1].

Nanotehnologija omogućava razvoj novih tipova senzora sa izuzetnom preciznošću, koji se koriste u širokom spektru uređaja, od pametnih telefona do medicinskih aparata. Ovaj projekat istražuje kako nanotehnologija omogućava miniaturizaciju senzora i poboljšanje njihovih performansi.

The project focuses on planning methods for robots that must solve complex, multi-step tasks in structured or semi-structured environments, potentially under visual and language-based task specifications. Long-horizon robotic problems require reasoning across multiple levels of abstraction, including task sequencing, symbolic consistency, geometric feasibility, and execution-aware replanning. Task and motion planning provides a principled framework for addressing these challenges by combining high-level symbolic reasoning with low-level motion generation and feasibility checking. This internship emphasizes AI planning methods, including search algorithms, PDDL-based modeling, and related approaches to sequential decision-making in robotics. Depending on the project scope, vision-language models (VLMs) may also be incorporated for semantic grounding, subgoal proposal, perception, or search guidance.

The project addresses a central challenge in embodied intelligence: enabling robots to interpret language instructions, ground them in visual observations, and execute coherent multi-step behaviors over extended temporal horizons. Recent advances in multimodal learning have led to strong progress in language-conditioned robotic control. However, long-horizon tasks remain challenging due to compounding errors, partial observability, delayed dependencies, and the need for robust subgoal reasoning, temporal abstraction, and recovery from failure. This internship focuses on learning-based approaches for building VLA systems that can support reliable sequential decision-making in complex robotic environments.

Kandidati/Kandidatkinje će zajedno sa mentorom analizirati druge kanale produkcije i raspada ovog leptokvarka. Tačnije, analizira ćemo druge moguće kombinacije produkcije i raspada koje uključuju kvarkove i leptone prve i druge generacije (odnosno kombinacije down-quarka/strangequarka sa elektronima ili mionima). Konačni rezultati bi trebali biti granice na parametarski prostor leptokvarka, odnosno na njegovu masu i na njegovu konstantu vezivanja). Naposljetku ćemo uporediti izvedene rezultate sa granicama koje proizilaze iz eksperimenata na niskim energijama.

U ovom projektu studentice i studenti će raditi na razvoju i optimizaciji metoda vještačke inteligencije (AI) za analizu i rekonstrukciju putanje rasutih gama zraka u detektorima visokih energija. Inspiracija dolazi iz postojećih rješenja opisanih u radovima gdje se koriste duboke neuronske mreže i probabilistički pristupi za prepoznavanje elektronskih tragova pri niskim energijama.

Primjena dubokog učenja za precizno određivanje spola u forenzičkoj odontologiji: Studija na dječjoj i adolescentskoj populaciji Identifikacija spola je ključna u forenzičkim istragama, posebno u slučajevima koji uključuju nepoznate osobe, nestale osobe ili žrtve gnusnih zločina. Neke studije sugeriršu da mogu postojati razlike u vremenu i brzini razvoja trećeg molara između muškaraca i žena.

Periphytic algae are essential components of aquatic ecosystems and have gained growing interest in forensic science due to their potential as biological tracers in investigations. This study explores the colonization dynamics of periphytic algae on various synthetic and natural substrates over a 56-day period in a natural freshwater environment. Substrates including cotton, nylon, leather, metal, plastic, glass, and wood were submerged to assess differences in algal succession and biofilm formation.

In this project, we are looking to compare this new implementation with the reference NumPy implementation, as well as the other performant NumPy implementations (e.g., Cython, Numba, Pythran, etc.) through the standardized benchmark sets such as NPBench.

Cilj ovog istraživačkog projekta je da se kroz multidisciplinarni pristup razviju i testiraju poboljšanja u dizajnu sistema Expert Experiments s fokusom na korisnički interfejs, prikupljanje podataka i primjenjivost u formalnim i neformalnim obrazovnim okruženjima (škole, kampovi, online kursevi). Pored toga, studenti će koristiti ovu platformu za prikupljanje podataka iz stvarnog okruženja (npr. temperatura tla, promjene u pritisku, detekcija kretanja) i evaluirati je kroz eksperimente sa srednjoškolcima i studentima.

Pruned modeli za obradu slika se široko koriste u različitim aplikacijama gdje su efikasnost, brzina i manja potrošnja memorije kritični npr. mobilni, IoT, medicinski uređaji itd.

Ciljani projekat ucenja analiziranja relevantnih clanaka iz oblasti sumarstva, s pracenjem ostalih izvora informacija o izvrsenju kriminaliteta unutar sumskih gazdinstava, lovista, kao i izvora informacija o kradji lovackih pasa, ilegalnoj distribuciji lovnih dobara, lovnih pasa i organizaciji drugih ilegalnih aktivnosti u sumama u BiH.

In this project, we are looking to apply Codon and its NumPy libraries to implement various LLMs, such as Gemini, Llama, Mixtral, and to compare the new implementations with the current state-of-the-art implementations in other languages.

The task of forestry companies is to contribute, together with representatives of non-governmental organizations and experts from the academic community, to knowledge about how climate change in Bosnia and Herzegovina affects forestry as an industrial branch, but also biodiversity. Sustainable management of forest ecosystems is a priority for local communities, administrative units, and the overall state strategy of each country. In Bosnia and Herzegovina, according to the latest Large-Scale Forest Inventory (Lojo et al., 2008), natural forests occur on over 50 percent of the total territory of the country. The survival of forest tree species is closely linked to the survival of wild animal species. Each statistical contribution to the knowledge on individual species behavior in the conservation context is valuable and desirable. In terms of exploring the existence of drought-resistant individuals in our ecosystems, the study was aimed.

Health data are characterised by the large numbers of variables categorical in their nature. One-hot encoding is commonly used in health data analysis when dealing with categorical variables. It is a popular technique used in machine learning to represent categorical variables numerically. It converts categorical variables into binary vectors, where each category is represented by a binary value (0 or 1) in a separate feature. While one-hot encoding can be useful in certain scenarios, it can also have an impact on model performance e.g., exacerbate the curse of dimensionality problem and model complexity and interpretability. This project aims to develop novel encoding method using real health data and compare it against the conventional such as one-hot encoding and embedding-based.

Metastatska bolest odgovorna je za preko 90% smrti od raka, ali genomske determinante koje određuju koji primarni tumori će metastazirati i u koje organe i dalje nisu dovoljno razjasnjene. Karcinom bubrega (RCC) je posebno pogodan model za ovu vrstu analize: pokazuje izrazito stereotipne obrasce diseminacije (pluća, kosti, mozak, jetra, limfni čvorovi) i ima dobro definisan skup driver/pokretačkih gena: VHL, PBRM1, BAP1, SETD2, KDM5C, MTOR.

Every solar panel in a real installation is different from its neighbours at any given moment. Dust, partial shadow, aging, and temperature gradients all cause individual panels to produce less than their rated peak, and by different amounts at the same time. Getting the maximum available power out of the array as a whole is a harder problem than it looks.

Cilj ovog istraživanja je sistematski analizirati sve Bayesian optimizacijske algoritme (HPO metode) koji su testirali LassoBench, kako bi se utvrdilo koji metod najbolje funkcionira u kontekstu Weighted Lasso regresije.

Vještačka močvara Bistrik na području općine Kakanj predstavlja specifičan antropogeni ekosistem nastao ljudskim djelovanjem (rudarske aktivnosti, eksploatacija ili hidro-morfološke izmjene). Iako je vještačkog porijekla, ovaj lokalitet je s vremenom preuzeo ulogu sekundarnog refugijuma za brojne močvarne organizme, uključujući ptice, gmizavce, vodozemnce, makrofite i mikroskopske alge. Međutim, uslijed antropogenog pritiska, eutrofizacije i potencijalnog zagađenja teškim metalima ili otpadnim vodama, ekosistem se suočava sa vidljivom degradacijom. Ovaj projekat istražuje mogućnosti ekološke restauracije močvare Bistrik primjenom prirodom inspirisanih rješenja (Nature-based Solutions - NbS) i ekološkog inženjerstva.

Image classification is an important task in computer vision field. However, current image classification models are not human understandable. The goal of this research is to achieve explainability via diffusion based concept swapping.