About the Role:
We are seeking experienced researchers and scientists to contribute to the development and growth of a prominent journal in the field of machine learning.
The ideal candidate will have a strong background in machine learning research, particularly in clinical, medical, and pharmaceutical research, as well as related subject areas.
Key responsibilities include actively contributing to the selection of topics, reviewers, and authors, as well as soliciting Special Thematic Issues on trending topics.
Additionally, the successful candidate will review articles submitted to the journal in their area of expertise at least thrice a year.
Requirements:
A Ph.D. in a relevant field with experience in Machine Learning Research.
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 the English language.
Benefits:
Saving APCs on publishing your research by being entitled to publish your papers and thematic issues free of cost.
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.
Networking opportunities with professionals, scholars, and experts on the editorial board, opening new collaborations and broadening your perspective in the field.
About the Journal:
The journal publishes critical and authoritative reviews/mini-reviews, original research, and methodology articles, as well as thematic issues in various 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, 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.