Contract Senior Applied AI Scientist

Key Skills

Aartificial intelligence, Deep Learning, Github, Machine learning techniques, NeurIPS, Optimization

Job Description

Come join a team of industry and science leaders to achieve a vision of empowering innovation through state-of-the-art artificial intelligence and machine learning.

aiXplain is looking for a Contract Senior Applied AI Scientist who would be instrumental in creating personalized protection strategies for motor applications. Your expertise in time-series analysis, signal processing, machine learning, deep learning, reinforcement learning, and optimization will drive the development of tailored protection policies, ensuring optimal circuit breaker performance and minimizing disruptions. This will require strong problem-solving and communication skills for multi-disciplinary teamwork.

This is a 3-6 month remote part-time contract opportunity with the potential for extension and ramp up of hours.

Responsibilities

• Innovate protection strategies: Analyze signals / time-series for differentiation between inrush and short-circuit events.
• Tailored protection design: Utilize signal processing and optimization skills to craft protection functions for individual devices.
• AI-powered policies: Employ machine learning, deep learning, and reinforcement learning to formulate circuit breaker protection policies.
• Drive collaboration: Work with domain experts to translate requirements into effective protection policies, ensuring robustness.
• Enhanced transparency: Lead the use of techniques like SHAP analysis to enhance model transparency and interpretability.
• Comprehensive documentation: Document research methodologies and their findings to create technical reports and documentation.

Qualifications

• Education: Ph.D. or Master’s degree in Electrical Engineering, Computer Science, Mechatronics, or related fields with a focus on AI/ML.
• Expertise: Extensive experience in time-series analysis, signal processing, machine learning, deep learning, and reinforcement learning.
• Research excellence: Demonstrated research in AI/ML through top-tier conference publications (NeurIPS, ICML, etc.) and GitHub contributions.
• Model proficiency: Mastery of classical and deep machine learning algorithms, reinforcement learning, and optimization techniques.
• Architecture skills: Proven ability to implement convolutional, recurrent, variational, generative, and transformer architectures.
• Unsupervised learning: Experience in unsupervised, semi-supervised, and self-supervised deep learning.
• Deep architectures: Experience in variational, adversarial, flows-based, and diffusion generative model architectures.
• Electrical engineering: Knowledge of circuit protection devices and electrical engineering concepts is advantageous.
• Optimization knowledge: Knowledge in gradient-based and blackbox optimization, including meta-learning and evolutionary methods.