About Editorial Board Membership
We are seeking experienced researchers and scientists to join our journal as Editorial Board Members for Current Machine Learning.
As an Editorial Board Member, you will 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).
Responsibilities include reviewing articles submitted to the journal (at least thrice a year) in your area of expertise.
Requirements:
A scientist or researcher (PhD) with experience in Machine Learning Research in clinical, medical, and pharmaceutical research, and related subject areas is required.
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 required.
Ability to communicate clearly and timely with stakeholders in English is essential.
Benefits:
Save APCs on publishing your research
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
Editorial Board Members will have access to and review new research/review papers as they are submitted to the journal, keeping them abreast of the latest trends in Machine Learning Research in clinical, medical, and pharmaceutical research, and related subject areas.
Nurture 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
* Active Learning
* Adversarial Machine Learning
* Anomaly Detection
* Applications in Finance
* Applications in Healthcare
* Applications in Robotics
* AUtomated Machine Learning (AutoML)
* Bayesian Methods in Machine Learning
* Computational Learning Theory
* Computer Vision
* Data Preprocessing and Augmentation
* Deep Learning
* Dimensionality Reduction
* Ensemble Methods
* Ethics and Fairness in AI
* Evolutionary Algorithms
* Feature Selection and Extraction
* Federated Learning
* Game Theory for Machine Learning
* Generative Adversarial Networks
* Generative AI
* Graph-based Learning
* Interpretability and Explainability
* Large Language Models
* Meta-learning
* Model Evaluation and Validation
* Muti-task Learning
* Natural Language Processing
* Neural Architecture Search