Editorial Board Member - Machine Learning Journal
We are seeking experienced researchers and scientists to join our editorial board as Editorial Board Members for a leading machine learning journal.
About the Role:
The successful candidates will actively contribute to the development and growth of the journal by providing valuable scholarly input, including the selection of topics, reviewers, and authors. They will also contribute/solicit Special Thematic Issues on trending topics and review articles submitted to the journal in their area of expertise.
Requirements:
Candidates should have a PhD with experience in Machine Learning Research in clinical, medical, and pharmaceutical research, and related subject areas. They should also have at least 5 years of experience in peer-reviewing, editing, and writing research papers, along with a verifiable record of publications in peer-reviewed journals indexed in WOS Core Collection and/or Scopus. Strong communication skills in English are essential.
Benefits:
As an Editorial Board Member, you will be entitled to publish your papers and thematic issues free of cost. You will also have access to new research/review papers as they are submitted to the journal, allowing you to stay up-to-date with the latest trends in Machine Learning Research. Additionally, 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:
The journal publishes critical and authoritative reviews/mini-reviews, original research and methodology articles, and thematic issues in areas of machine learning. It 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 current advances and future perspectives from diverse disciplines that intersect with machine learning. We cover areas such as Active Learning, Adversarial Machine Learning, Anomaly Detection, Applications in Finance, Healthcare, Robotics, AutoML, Bayesian Methods, Computational Learning Theory, Computer Vision, Data Preprocessing, Deep Learning, Dimensionality Reduction, Ensemble Methods, Ethics and Fairness, Evolutionary Algorithms, Feature Selection, Federated Learning, Game Theory, Generative Adversarial Networks, Generative AI, Graph-based Learning, Interpretability, Large Language Models, Meta-learning, Model Evaluation, 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.