Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Понятная исследовательская роль с выходом в production в климатических технологиях, сильными бенефитами и современным ML-стеком. Но без зарплатного диапазона сложнее оценить финансовые условия.
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Зарплата не указанаСовременный стекПонятные задачиКлиматические технологииСильный соцпакетГибкий формат
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Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
Research Scientist (AI): Developing modelling, identification, optimisation, and control methods for autonomous AI systems that improve the efficiency, stability, and emissions of heavy industrial plants with an accent on system identification, Bayesian optimisation, and physics-informed machine learning. Focus on designing rigorous experiments, learning plant dynamics under uncertainty, transferring methods across plants, and deploying algorithms in collaboration with process and machine learning engineers.
Location: Hybrid in London, United Kingdom
Company
hirify.global develops autonomous AI control and optimisation systems that use manufacturing physics to reduce carbon emissions and improve the efficiency and stability of heavy industrial plants such as cement, steel, and glass facilities.
What you will do
Shape the research and development agenda by turning challenges from live industrial plants into well-defined research problems.
Develop and implement methods for modelling, system identification, optimisation, and control of complex physical systems.
Design rigorous experiments, establish strong baselines, and determine when and why methods work.
Work with process engineers to incorporate plant physics, operating knowledge, and constraints into models and algorithms.
Collaborate with machine learning and deployment engineers to evaluate methods and bring them into production.
Monitor deployed performance, investigate failures, guide further research, and communicate findings clearly.
Requirements
PhD in control, machine learning, applied mathematics, engineering, physics, or a related field, or equivalent research experience.
Strong research background in one or more of control theory, data-driven control, system identification, Bayesian optimisation, probabilistic modelling, physics-informed machine learning, imitation learning, or model-based reinforcement learning.
Evidence of formulating research questions, developing methods, and evaluating them carefully through publications, open-source work, or applied research.
Strong Python skills and experience implementing machine learning models using tools such as Pandas, NumPy, SciPy, and PyTorch.
Interest in working closely with engineers and taking responsibility for how methods perform in practice.
Practical experience deploying machine learning and control algorithms.
Strong interest in climate mitigation.
Nice to have
Experience or knowledge related to cement is not required; willingness to learn from operators and process engineers is valued.
Culture & Benefits
Flexible working arrangements coordinated with the team.
Share options and equity in the company.
30 days of holiday plus bank holidays.
Generous pension scheme.
Culture based on high standards, ownership, honesty, kindness, and enjoyment of challenging work.
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