Editorial Board Member - Machine Learning Journal
We are seeking experienced researchers and scientists to join us as Editorial Board Members for the journal, Current Machine Learning.
This is a unique opportunity to contribute to the development and growth of the journal by providing valuable scholarly input, including the selection of topics, reviewers, and authors.
* Actively contribute to the journal's editorial process
* Select topics, reviewers, and authors for submission
* Review articles submitted to the journal in your area of expertise
Requirements:
* PhD in a relevant field 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
* Ability to communicate clearly and timely with stakeholders in the English language
Benefits:
* Publish your papers and thematic issues free of cost
* Access and review new research/review papers as they are submitted to the journal
* Network with professionals, scholars, and experts on our editorial board
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:
* Active Learning
* Adversarial Machine Learning
* Anomaly Detection
* Applications in Finance
* Applications in Healthcare
* Applications in Robotics
* AutoML (Automated Machine Learning)
* 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