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… more

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

   Shared topics: Metaheuristic Optimization Algorithms Research

2. %47
   SONER KIZILOLUK

   Doktor Öğretim ÜyesiMalatya Turgut Özal Üniversitesi

   Shared topics: Metaheuristic Optimization Algorithms Research

3. %45
   FUNDA KUTLU ONAY

   DoçentAmasya Üniversitesi

   Shared topics: Metaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms

4. %45
   HARUN BİNGÖL

   DoçentMalatya Turgut Özal Üniversitesi

   Shared topics: Metaheuristic Optimization Algorithms Research

5. %45
   YAĞMUR ÖLMEZ

   Doktor Öğretim ÜyesiMalatya Turgut Özal Üniversitesi

   Shared topics: Metaheuristic Optimization Algorithms Research · Robotic Path Planning Algorithms

6. %43
   FERHAT KUTLU

   Doktor Öğretim ÜyesiBeykoz Üniversitesi

   Shared topics: Natural Language Processing Techniques

7. %43
   HASAN BADEM

   DoçentKahramanmaraş Sütçü İmam Üniversitesi

   Shared topics: Metaheuristic Optimization Algorithms Research

8. %43
   SEVGİ AKTEN KARAKAYA

   Öğretim Görevlisi (uygulamalı Birim)Düzce Üniversitesi

   Shared topics: Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications

9. %43
   UĞUR ÇEKMEZ

   Doktor Öğretim ÜyesiBiruni Üniversitesi

   Shared topics: Robotic Path Planning Algorithms · Metaheuristic Optimization Algorithms Research · Distributed Control Multi-Agent Systems

10. %42
    ESRA ŞATIR

    DoçentDüzce Üniversitesi

    Shared topics: Metaheuristic Optimization Algorithms Research

11. %42
    OSMAN ALTAY

    DoçentManisa Celâl Bayar Üniversitesi

    Shared topics: Metaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms

12. %41
    NURGÜL GÖKGÖZ KÜÇÜKSAKALLI

    Doktor Öğretim ÜyesiÇankaya Üniversitesi

    Shared topics: Matrix Theory and Algorithms · Advanced Optimization Algorithms Research · Mathematical and Theoretical Epidemiology and Ecology Models

13. %40
    HASAN ÖZTÜRK

    Doktor Öğretim ÜyesiBartın Üniversitesi

    Shared topics: Natural Language Processing Techniques

14. %40
    MUHAMMAD UMER KHAN

    DoçentAtılım Üniversitesi

    Shared topics: Robotic Path Planning Algorithms · Distributed Control Multi-Agent Systems

The fit is computed by code on every request: semantic similarity (55%) between the call text (title, EU keywords, start of the official description) and the academic’s YÖKSİS fields, keywords and OpenAlex topics, plus shared distinctive terms (45%; common words weigh little). Below 40% is not shown. A subject hint only — not an eligibility, capacity or merit assessment. Hidden profiles are never listed.