PhD Research Assistant – Artificial Intelligence & Machine Learning (AI/ML)

Position:* PhD Research Assistant – AI/ML

*Department:* Research & Development

*Employment Type:* Full-Time / Part-Time / Research Contract

*Work Mode:* Remote / Hybrid

*Reports To:* PhD Research Scholar / Principal Researcher

Position Overview

We are seeking a highly motivated and technically proficient *AI/ML Research Assistant* to support advanced doctoral-level research in Artificial Intelligence, Machine Learning, Deep Learning, and Data Science.

The candidate will assist in conducting literature reviews, developing and evaluating machine learning models, performing data analysis, designing research experiments, preparing technical documentation, and supporting the publication of high-quality academic research papers.

The ideal candidate will have strong analytical and programming skills, a solid understanding of research methodologies, and experience working with academic publications, particularly IEEE, Springer, Elsevier, and other reputable scholarly outlets.

Key Responsibilities

1. Academic Research & Literature Review

• Conduct comprehensive literature reviews using IEEE Xplore, SpringerLink, ScienceDirect, ACM Digital Library, Google Scholar, and Scopus.

• Identify research gaps, emerging AI/ML techniques, and opportunities for original research.

• Develop systematic literature reviews, comparative studies, and research summaries.

• Maintain organized research references using Zotero, Mendeley, or EndNote.

2. Machine Learning & Deep Learning Development

• Design, implement, and evaluate machine learning models using Python.

• Work with supervised, unsupervised, and ensemble learning techniques.

• Develop deep learning models using TensorFlow, PyTorch, or Keras.

• Experiment with Random Forest, XGBoost, LightGBM, neural networks, CNNs, RNNs, LSTMs, Transformers, and other relevant architectures.

• Apply hyperparameter optimization, feature selection, and model validation techniques.

3. Data Analysis & Research Experimentation

• Identify, collect, clean, preprocess, and analyze research datasets.

• Design reproducible experiments with appropriate baselines and statistical validation.

• Evaluate models using precision, recall, F1-score, ROC-AUC, PR-AUC, and other suitable metrics.

• Apply class imbalance techniques including SMOTE, ADASYN, and cost-sensitive learning.

• Prepare comparative performance analysis, graphs, statistical tables, and research visualizations.

4. PhD Research & Dissertation Support

• Assist in structuring research proposals, research objectives, hypotheses, and methodology sections.

• Support the preparation of doctoral progress reports, Research Advisory Committee (RAC/RDC) presentations, and research documentation.

• Organize experimental findings and maintain technical research records.

• Assist with dissertation documentation, referencing, formatting, and technical review.

• Support the scholar in documenting original research contributions while following university academic integrity requirements.

5. Research Paper Preparation & Publication

• Support the preparation of manuscripts for IEEE, Springer, Elsevier, and Scopus-indexed journals and conferences.

• Prepare technical sections, experimental results, literature comparisons, tables, and figures based on verified research findings.

• Format manuscripts using IEEE, Springer LNCS, LaTeX, and other publication templates.

• Assist in preparing responses to peer-review comments.

• Verify references, citations, research originality, and submission requirements.

• Maintain a tracker of manuscripts, submissions, revisions, and publication deadlines.

6. Research Innovation & Prototype Development

• Explore new AI/ML architectures, optimization approaches, and hybrid models.

• Develop proof-of-concept prototypes and experimental pipelines.

• Research applications such as financial transaction fraud detection, anomaly detection, explainable AI, cybersecurity, and intelligent automation.

• Support the technical documentation of potentially patentable research outcomes.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, or a related field.

• Preferably M.Tech, M.Sc., or an individual currently pursuing a PhD in AI/ML.

• Strong Python programming skills.

• Experience with Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, and Jupyter Notebook.

• Sound…