Quantum Machine Learning

Expected Outcome: Integration of quantum computing into data pre-processing pipelines and learning workflows for data-heavy or computationally intensive tasks, demonstrating clear improvements in processing speed, computational complexity, modelling accuracy, and reduced sample requirements at scales achievable with NISQ-era devices, Reliable and scalable Quantum Machine Learning (QML) models and algorithms, integrated with existing AI frameworks and pipelines, enabling faster data processing, improved prediction accuracy, and enhanced computational capabilities, Validated quantum-enhanced AI methods demonstrating measurable improvements over classical baselines in terms of speed, accuracy, data efficiency, complexity, or scalability, supported by rigorous benchmarking and complexity analysis, Robust, noise-aware QML techniques suitable for NISQ hardware , including error-mitigation stra… more

1. %80
   AYŞENUR TÜRKMEN

   Doktor Öğretim ÜyesiBilecik Şeyh Edebali Üniversitesi

   Shared topics: Quantum Information and Cryptography · Quantum Mechanics and Applications · Quantum Computing Algorithms and Architecture

2. %70
   MERYEM SOYSALDI ŞAHİN

   Doktor Öğretim ÜyesiOndokuz Mayıs Üniversitesi

   Shared topics: Quantum Computing Algorithms and Architecture · Quantum Information and Cryptography

3. %69
   AMMAR DAŞKIN

   Doçentİstanbul Medeniyet Üniversitesi

   Shared topics: Quantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Quantum-Dot Cellular Automata · Quantum and electron transport phenomena

4. %66
   FAHRİYE GEMCİ

   Doktor Öğretim ÜyesiKahramanmaraş Sütçü İmam Üniversitesi

   Shared topics: Machine Learning in Bioinformatics

5. %65
   RASİM DERMEZ

   ProfesörAfyon Kocatepe Üniversitesi

   Shared topics: Quantum Information and Cryptography · Quantum Mechanics and Applications · Quantum Computing Algorithms and Architecture · Quantum optics and atomic interactions

6. %65
   YAĞMUR AKIN YILDIRIM

   Doktor Öğretim ÜyesiBartın Üniversitesi

   Shared topics: Advanced Memory and Neural Computing · Quantum Dots Synthesis And Properties

7. %64
   IŞIL ÜNALDI

   Araştırma GörevlisiOndokuz Mayıs Üniversitesi

   Shared topics: Radiomics and Machine Learning in Medical Imaging

8. %62
   AYLA GÜLCÜ

   DoçentBahçeşehir Üniversitesi

   Shared topics: Machine Learning and Data Classification · Domain Adaptation and Few-Shot Learning

9. %61
   SEVDA GÜL

   Öğretim Görevlisi (ders Vermekle Görevli)Sakarya Üniversitesi

   Shared topics: Radiomics and Machine Learning in Medical Imaging · EEG and Brain-Computer Interfaces

10. %60
    MERYEM NUR MORGÜL TUMBAZ

    Araştırma GörevlisiSakarya Üniversitesi

    Shared topics: Machine Learning in Materials Science

11. %58
    OSMAN UĞUR SEZERMAN

    ProfesörAcıbadem Mehmet Ali Aydınlar Üniversitesi

    Shared topics: Machine Learning in Bioinformatics

12. %56
    FULDEM MUTLU

    DoçentSakarya Üniversitesi

    Shared topics: Radiomics and Machine Learning in Medical Imaging

13. %56
    HÜSEYİN AKKAYA

    DoçentOndokuz Mayıs Üniversitesi

    Shared topics: Radiomics and Machine Learning in Medical Imaging

14. %55
    YASİN YÜCEL

    ProfesörHatay Mustafa Kemal Üniversitesi

    Shared topics: Quantum Dots Synthesis And Properties

15. %55
    ZAFER AYDIN

    ProfesörAbdullah Gül Üniversitesi

    Shared topics: Machine Learning in Bioinformatics · Computational Drug Discovery Methods

16. %54
    MEHMET ŞAHİN

    ProfesörAbdullah Gül Üniversitesi

    Shared topics: Semiconductor Quantum Structures and Devices · Quantum Dots Synthesis And Properties · Quantum and electron transport phenomena

17. %53
    EMİN DEMİREL

    DoçentAfyonkarahisar Sağlık Bilimleri Üniversitesi

    Shared topics: Radiomics and Machine Learning in Medical Imaging

18. %53
    FARUK ENES OĞUZ

    Öğretim Görevlisi (ders Vermekle Görevli)Hatay Mustafa Kemal Üniversitesi

    Shared topics: EEG and Brain-Computer Interfaces · Radiomics and Machine Learning in Medical Imaging

19. %52
    AHMET ALKAN

    ProfesörKahramanmaraş Sütçü İmam Üniversitesi

    Shared topics: EEG and Brain-Computer Interfaces · Radiomics and Machine Learning in Medical Imaging

20. %52
    ALİ KELEŞ

    ProfesörAğrı İbrahim Çeçen Üniversitesi

    Shared topics: Intelligent Tutoring Systems and Adaptive Learning · Radiomics and Machine Learning in Medical Imaging

21. %52
    SERDAR DURDAĞI

    ProfesörBahçeşehir Üniversitesi

    Shared topics: Computational Drug Discovery Methods · Machine Learning in Materials Science

22. %52
    SÜREYYA AKYÜZ

    ProfesörBahçeşehir Üniversitesi

    Shared topics: Machine Learning and Data Classification

23. %52
    ZEYNEP BANU ÖZGER

    DoçentKahramanmaraş Sütçü İmam Üniversitesi

    Shared topics: Machine Learning in Bioinformatics · Computational Drug Discovery Methods

24. %51
    ALİ İHSAN GÖKER

    ProfesörBilecik Şeyh Edebali Üniversitesi

    Shared topics: Quantum and electron transport phenomena · Semiconductor Quantum Structures and Devices

25. %51
    FATMA AKALIN

    Doktor Öğretim ÜyesiSakarya Üniversitesi

    Shared topics: Machine Learning in Bioinformatics

26. %51
    MEHMET AZİZ YİRİK

    Doktor Öğretim ÜyesiBahçeşehir Üniversitesi

    Shared topics: Computational Drug Discovery Methods · Machine Learning in Materials Science

27. %50
    AHMET HAŞİM YURTTAKAL

    DoçentAfyon Kocatepe Üniversitesi

    Shared topics: Radiomics and Machine Learning in Medical Imaging

28. %50
    HAKAN ÖZCAN

    Doktor Öğretim ÜyesiAmasya Üniversitesi

    Shared topics: Online and Blended Learning · Radiomics and Machine Learning in Medical Imaging

29. %50
    METEHAN ÖNAL

    Öğretim Görevlisi (ders Vermekle Görevli)Bilecik Şeyh Edebali Üniversitesi

    Shared topics: Quantum Dots Synthesis And Properties

30. %49
    AHMET FARUK YAZICI

    Araştırma GörevlisiAbdullah Gül Üniversitesi

    Shared topics: Quantum Dots Synthesis And Properties

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.