The Wallenberg-NTU Postdoctoral Fellowship is to bring outstanding young researchers, who have graduated from a Swedish university, to NTU for two years of postdoctoral research and studies. The Postdoctoral Fellows are given the opportunity to participate in a broad range of interdisciplinary activities and programs that characterize NTU's approach to research and education.

The fellow in this research project is required to:

Develop and implement advanced process and energy system models for integrated, low-carbon, and net-zero energy applications.
Conduct research on energy systems integration and sector coupling across electricity, heat, and fuels, including Power-to-X pathways (e.g., hydrogen and synthetic fuels).
Design and apply advanced optimization and control methods, including model predictive control (MPC), data-driven approaches, and physics-informed models.
Perform dynamic simulation and techno-economic and environmental assessment under real operating conditions and uncertainty.
Investigate geothermal and hybrid renewable energy integration within multi-energy and process systems.
Lead and manage research projects and work packages, ensuring the timely delivery of outputs and alignment with objectives.
Prepare and contribute to competitive research proposals and funding applications.
Supervise and mentor PhD, MSc, and undergraduate students.
Collaborate with interdisciplinary teams and external partners from academia and industry.
Disseminate research outcomes through high-impact publications, conferences, and technical reports.

Job Requirements:

A PhD in Mechanical Engineering, Energy Engineering, Civil and Architectural Engineering, or a closely related field.
Strong expertise in process systems engineering, energy systems integration, and thermal sciences.
Knowledge of Power-to-X technologies, sector coupling, and sustainable energy systems.
Experience in geothermal and hybrid renewable energy systems.
Strong background in optimization, control, and system dynamics.
Experience in modeling, simulation, and optimization of complex energy and process systems.
Experience in applying AI and data-driven methods in engineering systems.
Strong publication record and demonstrated research contributions in relevant fields.
Proficiency in programming languages such as Python and MATLAB.
Experience with system modeling tools such as TRNSYS, Modelica, or equivalent platforms.
Strong analytical, problem-solving, and scientific communication skills.
Ability to lead research activities and manage projects independently.
Strong supervision and mentoring capabilities.
Ability to work effectively in multidisciplinary and collaborative research environments.
Familiarity with Information Theory (specifically entropy concepts) as applied to model confidence calibration.

We regret to inform that only shortlisted candidates will be notified.