PhD student in Energy and Environmental Engineering

Mälardalen University (MDU) is the youngest university in Sweden. In line with our vision, to be a progressive and collaborative University where we shape a sustainable future together, we wish to make a difference. Do you want to be involved and contribute to our development? Together, we can create a sustainable future through knowledge and innovation. We believe that knowledge and new perspectives are best attained and reached together in collaboration with others – our colleagues, students, the private and public sectors, both nationally and internationally. At the school of Business, Society and Engineering, our students study for, among other things, university and civil engineers, political scientists and economists. With us, the research focuses are industrial economics and organization and the energy of the future. Our work takes place in collaboration and in strategic agreements with companies, organizations and authorities in the region.Employment information Employment: Temporary employment Scope: Full time Number of positions: Closing date for application: 2024-11-24 Campus location: Västerås School: School of Business, Society and Engineering, (EST)   Eligibility Access to third-cycle courses and study programmes at a higher education as well as basic eligibility requirements and assessment criteria, are regulated in the Higher Education Ordinance chapter 7 §§ 34–41(SFS 1993:100). More information about third-cycle studies at MDU A full-time employment as a doctoral student is attached to this third-cycle studies which corresponds to four years.  Position description We are seeking a PhD student to join our research team specializing in multiphase flow analysis. The research focuses on developing and applying advanced machine learning techniques for computer vision to enhance the understanding and modeling of multiphase flows, which are crucial in various industrial processes. The successful candidate will work on integrating state-of-the-art machine learning algorithms with computer vision methods to analyze and predict multiphase flow behavior. The project will require the candidate to work closely with a multidisciplinary team, potentially involving collaborations with external partners in academia and industry. By combining expertise in multiphase flow, machine learning, and computer vision, the research aims to advance both theoretical knowledge and practical applications, contributing to innovative solutions and future technological developments in this field. The PhD student work includes taking responsibility for carrying out the doctoral studies in accordance with the individual study plan that exists for each doctoral student. This includes conducting their research in consultation with their supervisors and in collaboration with other colleagues, as well as publishing research results in scientific journals. Within the framework of the doctoral education, the doctoral student must complete and pass 60 credits of courses. The doctoral student is also expected to participate in the department's training. This could be, for example, giving lectures, tutoring students, assisting in laboratories and calculation exercises, etc.               Qualifications For basic eligibility, the following is required: • A degree at an advanced level. • At least 240 higher education credits, of which at least 60 are at an advanced level. • Equivalent knowledge acquired in Sweden or abroad through other means. Specific eligibility requirements include having one of the following: • Completed a Master of Science in Engineering in a relevant technical field or other relevant field with a focus on energy systems, heat transfer processes, multiphase flow or similar areas • Completed a four-year natural science program with technical content equivalent to a Master of Science in Engineering • Acquired knowledge of substantially the same scope through other means, either within or outside the country Assessment criteria Prior to employment, consideration must be given to the ability to assimilate third-cycle courses and study programmes at a higher education. Experience in machine learning with application to image - signal analysis and system optimization is highly desirable. Very good knowledge of English, both spoken and written is requried.       In an overall assessment of suitability, emphasis is also placed on personal abilities. All employees at MDU are expected to cooperate and treat colleagues and students with respect, take responsibility for the organisation and their own work duties and contribute to a positive work environment. We are looking for a candidate with experience in working both independently and as part of an interdisciplinary team, along with a proven ability to engage industry partners and other stakeholders to apply research findings to practical solutions. Strong communication and presentation skills are also essential.  For the position the following is of high merit: • Knowledge in fluid mechanics, multiphase flow, heat transfer and thermodynamics. • Knowledge in AI and deep learning framework such as TensorFlow. • Good programming skills in Python and Matlab with their basic libraries for machine learning and image processing. • Familiar with Linux.Publications in relevant fields that demonstrate the ability to conduct and publish high-quality research and communicate results effectively. • Experience of teaching and/or supervision of students in energy engineering. • Knowledge of Swedish, both spoken and written. We value the qualities that an even distribution of age and gender, as well as ethnic and cultural diversity, can contribute to the organization. Application Application is made online. Make your application by clicking the "Apply" button below. The applicant is responsible for ensuring that the application is complete in accordance with the advertisement and will reach the University no later than closing date for application. We look forward to receiving your application. Union representatives: Saco-S saco-s@mdu.se Susanne Meijer ST-OFR/S, tel: 021-10 14 89 We decline all contact with recruiters and salespersons of advertisements. We have made our strategic choices for this recruitment.