Machine Learning/AI Research Engineer (Post-doc)

Data centres are becoming some of Europe's most consequential and fastest-growing electricity users, yet most still appear to the grid as large, opaque, and largely grid-agnostic loads. AIRCAS, short for AI-driven Resource & Cooling forCASting and control for data centres, gives you the opportunity to change this through advanced AI control in a live data-centre environment. You will help build a digital twin, forecasting, and safety-constrained control system that coordinates electricity demand, cooling, heat recovery, and water use.

At HPE Labs Ireland, the role will involve live telemetry processing, research, training, evaluation, and deployment of reinforcement-learning controllers across two MODPODs in the Customer Innovation Centre (CIC) data centre. You will validate control policies through replay, hardware-in-the-loop, shadow-mode, and controlled live trials. This is an applied research role spanning physical-system modelling, software engineering, machine learning, and operational deployment.

You will collaborate with our international project partners: the University of Porto, CNRS, Savery Tech, RWTH Aachen, Swerim, TerraBarrier, and Daikin Applied.

Hewlett Packard Labs is seeking a highly qualified, self-motivated machine-learning and AI researcher to join its growing AI research team in Ireland. The role will accelerate research on digital twins that incorporate machine learning, with a focus on the AIRCAS programme.

You will develop and deploy advanced reinforcement-learning control for a live data-centre environment. The work spans research, software engineering, and experimental validation: from data collection and physical-system modelling through forecasting, control-policy training, hardware-in-the-loop testing, shadow operation, and bounded live trials.

• →Build the CIC data and workload pipeline, including controlled workload generation, telemetry ingestion, time-series data management, data-quality checks, and baseline datasets.
• →Develop modular digital-twin software that combines site telemetry with physical models, empirical performance maps, cooling and storage models, and operational constraints.
• →Develop probabilistic forecasts for IT load, cooling demand, and relevant water and heat signals using CIC operational data.
• →Research, implement, train, and evaluate objective-shifting multi-objective reinforcement-learning policies that balance flexibility, cost, carbon, water use, and durability within hard safety limits.
• →Implement a price-response interface that produces feasible, auditable flexibility commitments with uncertainty information.
• →Validate the control stack through replay, hardware-in-the-loop testing, shadow mode, and controlled actuation windows on the CIC MODPODs.
• →Produce reproducible evidence for flexibility, reliability, sustainability, and durability performance, including measurement-and-verification workflows and KPI analysis.
• →Collaborate with HPE Labs researchers and the AIRCAS partners to integrate laboratory cooling results, sustainability signals, and physical operating guardrails into the control system.

Hewlett Packard Labs is HPE's central research organisation. Its purpose is to deliver breakthrough technologies that create a competitive advantage for HPE, investing in fundamental areas of strategic interest and preparing resulting technologies for adoption.

The AI Research Lab (AIRL) focuses on applied machine-learning research and applications relevant to HPE and its customers. Its work includes reinforcement learning for sustainability, digital twins, complex scientific applications such as fusion energy, large language models, trustworthy and responsible AI, and data foundations for AI. The team brings together diverse skills and expertise to explore ambitious research and technology opportunities.

We are looking for a machine-learning research engineer with experience in machine learning, time-series analysis, reinforcement learning, and digital twins. The role requires strong engineering judgement and an aptitude for modelling physical systems. The ideal candidate is entrepreneurial, comfortable with system-level analysis and design, and motivated to turn research ideas into deployed technology.

We expect team members to provide technical leadership, take innovative ideas from conception through design, development, implementation, evaluation, and technology transfer, and collaborate effectively with HPE Labs teams, external partners, and HPE's wider innovation community.

• PhD degree, or Master's degree with equivalent research and innovation experience, in a relevant discipline such as computer science, software engineering, electrical engineering, mathematics, physics, statistics, or mechanical engineering.
• Proven record of innovation in deep learning, scientific computing, engineering computation, or machine-learning applications.
• Demonstrated experience developing proofs of concept, first-of-a-kind solutions, or technologies for transfer into practice.
• Evidence of independent research leadership, for example through publications in leading conferences or journals.

• Strong software-development skills in Python and PyTorch.
• Strong experience applying machine learning to physical systems.
• Knowledge of digital twins, machine learning with digital twins, and sustainability applications.
• Strong science or engineering background, including system-level analysis and modelling.
• Sound understanding of machine-learning algorithms, data structures, and modern AI/ML platforms.
• Ability to develop integrated software algorithms; work with large-scale computing, data-analysis, and modelling environments; and translate technical specifications into robust code.
• Excellent research, development, written, and verbal communication skills in English.

• Research publications in machine learning, applied statistics, or AI.
• Experience designing, implementing, and operating AI/ML systems.
• Experience with reinforcement learning, probabilistic time-series forecasting, optimisation, or control.
• Experience with experimental systems, hardware-in-the-loop testing, or production software for research prototypes.

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Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability.

We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.

We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division.

We are unconditionally inclusive in the way we work and celebrate individual uniqueness. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.

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