We are seeking experienced researchers to join our editorial board as members of Current Machine Learning.
About the Role:
Our ideal candidate will actively contribute to the development and growth of the journal by providing valuable scholarly input, including the selection of topics, reviewers, and authors. They will also contribute/solicit special thematic issues on trending topics (one thematic issue every year) and review articles submitted to the journal (at least thrice a year) in their area of expertise.
The successful candidate will have a verifiable record of publications in peer-reviewed journals indexed in WOS Core Collection and/or Scopus, and be able to communicate clearly and timely with stakeholders in English.
Requirements:
* PhD in a relevant field with experience in machine learning research
* At least 5 years of experience in peer-reviewing, editing, and writing research papers
Benefits:
* Savings on publishing costs: As an Editorial Board Member, you will be entitled to publish your papers and thematic issues free of charge
* Access to new research: You will be able to access and review new research/review papers as they are submitted to the journal
* Networking opportunities: 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 include 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
24. Large Language Models
25. META-LEARNING
26. Model Evaluation and Validation
27. Multi-task Learning
28. Natural Language Processing
29. Neural Architecture Search
30. Object Detection
31. Online Learning
32. Optimization Techniques
33. Quantum Machine Learning
34. Recommender Systems
35. Reinforcement Learning
36. Retrieval-Augmented Generation
37. Robustness and Adversarial Machine Learning
38. Semi-parametric and Non-parametric Methods
39. Semi-supervised Learning
40. Speech Recognition
41. Statistical Learning Theory
42. Supervised Learning
43. Time Series Analysis
44. Transfer Learning
45. Unsupervised Learning