Editorial Board Member: Machine Learning Research
We are seeking experienced researchers and scientists to join our Editorial Board for a prestigious journal on machine learning.
1. About the Role:
o Contribute to the development and growth of the journal by providing scholarly input, including topic selection, reviewer choice, and author management.
o Solicit Special Thematic Issues on trending topics (one per year).
o Review articles submitted to the journal in your area of expertise (at least thrice annually).
Requirements:
To be eligible, you must have:
1. A PhD in Machine Learning Research with experience in clinical, medical, and pharmaceutical research, and related subject areas.
2. At least 5 years of experience in peer-reviewing, editing, and writing research papers.
3. A verifiable record of publications in peer-reviewed journals indexed in WOS Core Collection and/or Scopus.
4. The ability to communicate clearly and timely with stakeholders in English.
Benefits:
As an Editorial Board Member, you will enjoy:
1. Publishing Opportunities: Publish your research and thematic issues free of cost.
2. Access to New Research: Review new submissions and stay up-to-date with the latest trends in machine learning research.
3.
About the Journal:
Current Machine Learning publishes critical reviews, original research, methodology articles, and thematic issues on machine learning topics. The journal serves as an advanced forum for innovative studies and major trends in theoretical, methodological, and practical aspects of machine learning. Areas of interest include:
* 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
* Multi-task Learning
* Natural Language Processing
* Neural Architecture Search
* Object Detection
* Online Learning
* Optimization Techniques
* Quantum Machine Learning
* Recommender Systems
* Reinforcement Learning
* Retrieval-Augmented Generation
* Robustness and Adversarial Machine Learning
* Semi-parametric and Non-parametric Methods
* Semi-supervised Learning
* Speech Recognition
* Statistical Learning Theory
* Supervised Learning
* Time Series Analysis
* Transfer Learning
* Unsupervised Learning