EU Frontier AI Initiative: Advanced Scaling Frameworks for High-Performance AI Models (RIA) - Apply AI

Expected Outcome: This topic aims to advance the scientific and technical foundations for the scaling of AI models, enhancing the performance of training and inference processes and providing European AI labs with the methodologies required to compete at the global frontier. Project results are expected to contribute to some of the following outcomes: Establishment of robust scaling laws and predictive frameworks that allow for the reliable estimation of model performance before large-scale compute investment. Enhanced competitiveness of European advanced AI models through optimised dataset mixtures, refined architectures, and superior hyperparameter configurations. Optimised development for frontier AI models, achieved via automated and scientifically grounded scaling methodologies. Scope: To maintain a leading position in AI, Europe must master the methodologies required to scale AI mo… devamı

Algorithms, distributed, parallel and network algorithms, algorithmic game theoryArtificial intelligence, intelligent systems, multi agent systemsHigh performance computingMachine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)

1. %53
   PAKİZE ERDOĞMUŞ

   ProfesörDüzce Üniversitesi

   Ortak konular: Metaheuristic Optimization Algorithms Research

   Örtüşen yayınlar: An experimental comparison of the widely used pre\u2010trained deep neural networks for image classification tasks towards revealing the promise of transfer\u2010learning (2022) · Evaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Images (2024)

2. %45
   KEREM GENCER

   Doktor Öğretim ÜyesiAfyon Kocatepe Üniversitesi

   Çalışma konuları: Advanced Malware Detection Techniques · Artificial Intelligence in Healthcare and Education · COVID-19 diagnosis using AI

   Örtüşen yayınlar: Brain tumor classification with deep learning: A comparative study of AlexNet and EfficientNetB0 using principal component analysis (2025) · Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model (2025)

3. %45
   SEVGİ AKTEN KARAKAYA

   Öğretim Görevlisi (uygulamalı Birim)Düzce Üniversitesi

   Ortak konular: Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications

   Örtüşen yayınlar: pycellga: A Python package for improved cellular genetic algorithms (2025) · An Improved Cellular Genetic Algorithm with Machine-Coded Operators for Real-Valued Optimisation Problems (2024)

4. %43
   FUNDA KUTLU ONAY

   DoçentAmasya Üniversitesi

   Ortak konular: Metaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms

   Örtüşen yayınlar: Application of a metaheuristic gradient-based optimizer algorithm integrated into artificial neural network model in a local geoid modeling with global navigation satellite systems/leveling measurements (2022) · Solution of engineering design and truss topology problems with improved forensic-based investigation algorithm based on dynamic oppositional based learning (2024)

Uyum oranı kodla, her istekte hesaplanır. Çağrı metni (başlık, anahtar kelimeler, resmi açıklamanın başı) şu üç kaynakla karşılaştırılır: akademisyen profiliyle anlamsal yakınlık (%50), YÖKSİS anahtar kelimeleri ve OpenAlex konularındaki ortak ayırt edici terimler (%25) ve yayın içeriği — başlıklar, İngilizce özetler ve bunların Türkçe çevirileri (%25). Çeviriler sayesinde Türkçe çağrılar İngilizce yayınlarla da eşleşir. Yaygın kelimeler düşük ağırlıklıdır; %40 altı gösterilmez. Bir konu önerisidir; başvuru uygunluğu, kapasite ya da başarı değerlendirmesi değildir. Gizli profiller listelenmez.