Adversarial Machine Learning Researcher

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Requirements:

• Ph.D. or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Cybersecurity, or a related field.
• 2-5 years of experience in adversarial machine learning, cybersecurity, or related research area.
• Proficiency in machine learning frameworks like TensorFlow and PyTorch.
• Strong programming skills in Python, with experience in developing ML models.
• Deep understanding of deep learning architectures (CNNs, RNNs, GANs) and their vulnerabilities.
• Knowledge of adversarial attack techniques (FGSM, PGD, DeepFool) and defense strategies.
• Strong publication record in top-tier conferences or journals.
• Excellent analytical, problem-solving, verbal and written communication skills.

Responsibilities:

• Design, develop, and evaluate novel adversarial attack methods and defense mechanisms.
• Conduct research to propose new algorithms or improve existing ones.
• Publish research findings in top-tier conferences and journals.
• Collaborate with data scientists, machine learning engineers, and cybersecurity experts.
• Conduct thorough evaluations of existing ML models to identify adversarial vulnerabilities.
• Develop benchmarking frameworks to assess the effectiveness of adversarial defenses.
• Develop and maintain tools for generating adversarial examples and testing robustness.
• Automate testing and evaluation processes.
• Stay current with advancements in the field through literature reviews and conferences.