Job Title: AI Research Scientist — Energy Systems (1 Position)

About the Job
We are seeking an experienced, research-oriented AI Research Scientist to join our team and lead development of advanced machine learning and AI solutions for energy systems. The successful candidate will design, prototype and validate novel algorithms for energy optimization, forecasting, predictive maintenance, battery modelling, digital twins and grid-interactive control. This is a hands-on research role that bridges state-of-the-art AI methods and practical industrial deployments — you will publish, build prototypes, collaborate with engineering teams, and help translate research into production-grade solutions.

Responsibilities
- Lead research and development of ML/AI models for energy problems (demand forecasting, load balancing, scheduling, reinforcement learning for control, anomaly detection, prognostics).
- Develop physics-aware and hybrid models (ML + domain models) and digital-twin workflows integrating simulation and telemetry data.
- Design rigorous experiments, benchmark methods, and maintain reproducible research pipelines.
- Prototype and validate algorithms on real telemetry datasets (batteries, UPS, site load, IoT sensors) and partner with engineering to deploy models to edge/cloud.
- Work closely with product, data engineering, and operations teams to define success metrics, productionize models (CI/CD, monitoring) and run A/B evaluations.
- Publish in top-tier conferences/journals and represent the group at academic/industry events.
- Mentor junior researchers and engineers; review code and model designs; contribute to research strategy and roadmap.
- Ensure research follows safety, robustness and compliance best practices for industrial systems.

Qualifications
- PhD (preferred) or MSc with extensive research track record in Machine Learning, AI, Applied Mathematics, Control Systems, Electrical Engineering, or a closely related field.
- Strong background in time-series forecasting, reinforcement learning, probabilistic modelling, optimization and/or physics-informed ML.
- Proven research output (peer-reviewed papers, preprints) and experience translating research into prototypes or products.
- Hands-on proficiency in Python and ML frameworks (PyTorch, Tensor Flow, JAX) and tooling for experiments (MLFlow, Weights & Biases or similar).
- Experience with data engineering, feature engineering for telemetry, and model deployment (Docker, Kubernetes, cloud platforms, or edge inference).
- Familiarity with energy systems, power/grid concepts, battery modelling, UPS/industrial telemetry, or related domains is highly desirable.
- Strong problem-solving skills, experimental rigor, and ability to communicate technical results clearly to both technical and non-technical stakeholders.
- Excellent teamwork skills and willingness to work across research, engineering and product teams.

What We Offer
- Competitive compensation and performance-based incentives.
- Dedicated research time and resources (compute, data, lab access) and support for publications.
- Opportunity to work on high-impact industrial energy problems at the intersection of AI and physical systems.
- Collaborative, multidisciplinary team and mentorship opportunities.
- Support for conference travel, training and professional development.
- Flexible/hybrid working arrangements (as applicable).

How to Apply
Please submit the following to [اضغط هنا لمشاهدة البريد اﻹلكتروني] with subject line “AI Research Scientist — Energy Systems”:
1. CV (max 3 pages)
2. Cover letter (1 page) describing your research interests and relevant experience
3. Research statement / summary of recent projects (1–2 pages) and links to publications
4. Code or portfolio links (Git Hub, Git Lab) or example notebooks (if available)
5. Two references (name, role, contact)

Shortlisted candidates will be invited for an interview and asked to present a short research/prototype overview. We welcome applications from diverse backgrounds and encourage cross-disciplinary candidates to apply.