About the Role
Actively contribute to the development and growth of a prestigious machine learning journal by providing valuable scholarly input, including the selection of topics, reviewers, and authors.
Contribute/solicit Special Thematic Issues on trending topics in areas such as Active Learning, Adversarial Machine Learning, and more.
Review articles submitted to the journal in your area of expertise, ensuring high-quality content for our readers.
Requirements:
* PhD holder with experience in Machine Learning Research in clinical, medical, and pharmaceutical research, and related subject areas.
* At least 5 years of experience in peer-reviewing, editing, and writing research papers.
* Verifiable record of publications in peer-reviewed journals indexed in WOS Core Collection and/or Scopus.
* Able to communicate clearly and timely with stakeholders in the English language.
Benefits
As an Editorial Board Member, you will enjoy several benefits:
* Save APCs on publishing your research.
* Entitled to publish your papers and thematic issues free of cost.
* Access to new research/review papers as they are submitted, allowing you to stay up-to-date with the latest trends.
* Network with professionals, scholars, and experts on our editorial board, opening opportunities for collaboration and knowledge sharing.
About the Journal
Current Machine Learning publishes critical 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.
Our aim is to provide a comprehensive and reliable source of information on the current advances and future perspectives from diverse disciplines that intersect with machine learning.
Areas of interest cover but are not limited to: Active Learning, Adversarial Machine Learning, Anomaly Detection, Applications in Finance, Applications in Healthcare, and many more.
Current Machine Learning is an international, peer-reviewed journal on all aspects of machine learning, published continuously by Bentham Science Publishers.