Mohammed Alalawi

PhD Researcher @Canterbury Christ Church university, United Kingdom

Computer ScienceScientistData AnalystLecturerStatistical AnalysisML EngineerRenewable EnergyAlgorithms✔️ ITPythonAutomobile EngineerEngineering🧨 Consulting🧑‍🔬 EngineerElectrical engineerResearcher🧮 EducationUnited Kingdom

Mohammed Alalawi is a dedicated PhD Researcher at Canterbury Christ Church University in the United Kingdom. His research focuses on the intersection of sustainability, Battery Analytics, and deep learning, with a particular emphasis on State of Charge Estimation of Lithium Ion batteries. He has demonstrated a strong commitment to academic excellence and has contributed to several research projects and publications. Mohammed's work is characterized by its innovative approach, rigorous methodology, and practical implications for educators and policymakers. His research insights are valuable for those seeking to understand and leverage the potential of technology in education.

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Work Background

Field EnergyPhD Researcher intern

Oct. 2022 - Jan. 2023 Life Cycle Assessment (LCA): - Mastered the pre-existing LCA work for an energy storage site project. - Led the independent audit of the LCA, meeting industry standards and requirements and achieving full validation from a third-party before public disclosure. - Revised the 20-year carbon projection for one of the sites, ensuring accuracy. - Initiated the framework of comparing actual CO2 with forecasted for operational sites. - Collaborated with the Tech team to design a dynamic dashboard for tracking cumulative CO2 savings. o ESG Report and Performance Audit: - Single-handedly created the company's first-ever sustainability report for investors, including crucial metrics such as Scope I, II, and III GHG emissions. - Conducted a thorough analysis of various ESG reporting protocols to identify the most effective approach for the company. - Led an internal ESG performance audit, evaluating current practices and recommending areas of improvement.

Canterbury Christ Church university, United KingdomPhD Researcher

Nov. 2021Developed and applied innovative machine learning algorithms for SOC estimation, directly contributing to advancements in battery management and sustainable energy solutions. • Investigated the application of machine learning techniques, including deep learning models, to enhance SOC estimation accuracy and reliability, with a focus on second-life battery systems. • Published research findings in peer-reviewed journals on sustainable energy solutions. • Presented research findings at conferences, demonstrating expertise in battery SOC estimation and machine learning applications in the energy sector. International Research Collaboration • Collaborated with leading universities and research institutions on projects related to renewable energy and sustainable development. • Contributed as a data analyst and researcher in a multidisciplinary team focused on analysing PV generation data and optimizing solar energy systems. • Leveraged advanced data analytics techniques to extract insights and trends from multiple large-scale PV generation datasets, informing decision-making and improving system performan

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