About Editorial Board Member Roles
We are currently seeking experienced researchers and scientists to join us as Editorial Board Members for the journal, Current Machine Learning.
About the Role:
As an Editorial Board Member, you will actively contribute to the development and growth of the journal by providing valuable scholarly input, including the selection of topics, reviewers, and authors.
You will also contribute/solicit Special Thematic Issues on a trending topic (one thematic issue every year) and review articles submitted to the journal (at least thrice a year) in your area of expertise.
Requirements:
Scientist or researcher (PhD) with experience in Machine Learning Research in clinical, medical, and pharmaceutical research, and related subject areas is required.
Additionally, at least 5 years of experience in peer-reviewing, editing, and writing research papers is necessary. A verifiable record of publications in peer-reviewed journals indexed in WOS Core Collection and/or Scopus is also mandatory.
Benefits:
Publish Your Research Free of Charge
As an Editorial Board Member, you will be entitled to publish your papers and thematic issues, free of cost.
Stay Up-to-Date with the Latest Research
You will be able to access and review new research/review papers as they are submitted to the journal, allowing you to keep abreast of the latest trends in Machine Learning Research in clinical, medical, and pharmaceutical research, and related subject areas.
Network with a Community of Scholars
You will be able to connect with professionals, scholars, and experts on our editorial board, opening new opportunities to collaborate on novel research projects and broaden your perspective in the field.
About the Journal:
Current Machine Learning publishes critical and authoritative reviews/mini-reviews, original research and methodology articles, and thematic issues in areas of machine learning.
The journal serves as an advanced forum for innovative studies and major trends of theoretical, methodological, and practical aspects of machine learning.
Areas of interest cover, but are not limited to:
1. Active Learning
2. Adversarial Machine Learning
3. Anomaly Detection
4. Applications in Finance
5. Applications in Healthcare
6. Applications in Robotics
7. AUTOML (Automated Machine Learning)
8. Bayesian Methods in Machine Learning
9. Computational Learning Theory
10. Computer Vision
11. Data Preprocessing and Augmentation
12. Deep Learning
13. Dimensionality Reduction
14. Ensemble Methods
15. Ethics and Fairness in AI
16. Evolutionary Algorithms
17. Feature Selection and Extraction
18. Federated Learning
19. Game Theory for Machine Learning
20. Generative Adversarial Networks
21. Generative AI
22. Graph-based Learning
23. Interpretability and Explainability
25. Model Evaluation and Validation
26. MULTI-TASK LEARNING
27. Natural Language Processing
28. Neural Architecture Search
29. Object Detection
30. Online Learning
31. Optimization Techniques
32. Quantum Machine Learning
33. Recommender Systems
34. Reinforcement Learning
35. Retrieval-Augmented Generation
36. Robustness and Adversarial Machine Learning
37. Semi-parametric and Non-parametric Methods
38. Semi-supervised Learning
39. Speech Recognition
40. Statistical Learning Theory
41. Supervised Learning
42. Time Series Analysis
43. Transfer Learning
44. Unsupervised Learning