PRR Project

Summary

We are seeking a highly motivated Researcher to join our team in Mechanical Engineering Manufacturing for Circular Economy within TEMA and the Department of Mechanical Engineering. The candidate should focus on advancing the field of thermal sciences and thermal energy storage, namely through innovative machine learning algorithms within the scope of Manufacturing for Circular Economy. The candidate will not only collaborate with a multidisciplinary team of researchers to develop and apply innovative machine learning techniques to optimize thermal management systems, enhance thermal energy storage technologies, and address challenges in sustainable energy utilization in Manufacturing Processes but also share his research experience trough lectures and seminars. Key Responsibilities:Conduct research to develop novel machine learning algorithms tailored for thermal sciences and thermal energy storage applicationsIntegrate machine learning models into thermal management systems and thermal energy storage devicesAnalyze experimental and simulation data to identify patterns, optimize performance, and guide experimental designPublish research findings in high-impact scientific journals and present results at international conferences and seminarsMentor graduate and undergraduate students working on related projects and assist in their professional developmentLecturing to BSc, MSC and PhD courses on energy storage and machine learning techniquesStay abreast of the latest developments in machine learning, thermal sciences, and thermal energy storage technologies through permanent literature review and participation in relevant conferences and workshopsPrepare and submit research project proposals to secure external funding. Requirements:Ph.D. in Mechanical Engineering or in a related field with focus on machine learning applied to thermal energy storage and utilizationStrong background in machine learning, statistical modeling, and data analysisExperience with programming languages such as Python, MATLAB, or R for data analysis and algorithm development.Knowledge of thermal sciences, heat transfer, thermodynamics, and energy storage systemsFamiliarity with numerical simulation tools (COMSOL, ANSYS Fluent, or Open FOAM, etc.)Excellent written and verbal communication skills, with the ability to work effectively in a collaborative research environmentDemonstrated record of accomplishment of scholarly publications in peer-reviewed journals and conference proceedingsPrior experience in securing and managing research projectsAbility to multitask management, meet deadlines, and work independently with minimal supervision Scientific Profile:The ideal candidate for this position will have a strong scientific background in thermal sciences, thermal energy storage systems, and machine learning. They should be skilled at solving complex problems in these domains using advanced computational methods. Required competences include:Proficiency in developing and implementing machine learning algorithms, to analyze complex data sets and extract meaningful insights relevant to thermal management and thermal energy storagea solid background in thermal sciences, encompassing knowledge of heat transfer mechanisms, thermodynamics, fluid dynamics, and material properties relevant to thermal energy storage applicationsexperience in numerical simulations and computational modeling techniques used to study thermal systems and thermal energy storage devicesA record of accomplishment of interdisciplinary research that bridges the gap between machine learning and thermal sciences, demonstrating the ability to improve heat management strategies, and address sustainability challengesstrong analytical skills and the ability to interpret experimental and simulation data to validate machine learning models, guide experimental design and provide insights in thermal energy storage and thermal managementeffective communication skills, including the ability to disseminate research findings through high-impact publications and conference presentationscreativity, adaptability, and ability to tackle problems related to energy sustainability and thermal management, with a commitment to contributing to the development of solutions with societal impact. Rationale:Combining machine learning with thermal management and thermal energy storage research is justified by complexity, nonlinear dynamics, data-driven insights, optimisation, and cross-disciplinary synergies. Thermal systems´ complex interactions and nonlinear behaviours challenge conventional methods. Machine learning performs well with complexity, uncertainties, and large datasets and optimises system performance. Real-time thermal energy storage system control, material discovery, and cross-domain insights are possible. Machine learning can improve energy system efficiency, sustainability, and resilience.