Machine Learning Editorial Board Member
We are seeking experienced researchers and scientists to join our journal, Current Machine Learning, as Editorial Board Members.
The role involves actively contributing 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).
Additionally, you will review 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.
* At least 5 years of experience in peer-reviewing, editing, and writing research papers.
* A 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 English.
Benefits:
* Saving APCs on publishing your research. As an Editorial Board Member, you will be entitled to publish your papers and thematic issues free of cost.
* Keeping tabs on the latest research. Editorial Board Members will be able to access and review new research/review papers as they are submitted to the journal.
* Networking with 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.
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
* 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