Linköping University
4 days ago
PhD in Materials Science: Machine-Learning Accelerated Simulation of Hybrid Organic-Inorganic Interfaces Linköping University in Sweden
Degree Level
PhD
Field of study
Computer Science
Funding
PhD employment at Linköping University for four years full-time equivalent, initially for one year and then renewable based on progress; may extend up to five years depending on teaching and departmental duties. Starting salary is SEK 35,300 per month, with increases after 30% and 60% of doctoral studies. The position is connected to the WISE program and includes participation in the WISE Research School.
Deadline
Sep 25, 2026
Country
Sweden
University
Linköping University
How do I apply for this?
Sign in for free to reveal details, requirements, and source links.
Apply for this position
Keywords
Suggested positions
About this position
PhD opportunity in Materials Science at Linköping University, Sweden, focused on computational materials science, machine learning potentials, Hamiltonian learning, and molecular dynamics for hybrid organic-inorganic interfaces in sustainable energy devices.
The project aims to understand and optimize interfaces that control performance and stability in systems such as organic batteries and solar cells. You will study coupled electronic and ionic transport at atomistic interfaces, develop ML-accelerated simulation methods, and contribute to the design of stable materials for efficient charge and ion transport.
The work is based at the Laboratory of Organic Electronics (LOE) and within the newly established Computational Materials Science group of Assistant Professor Waldemar Kaiser, part of the Theory & Modelling unit led by Professor Igor Zozoulenko. The environment is multidisciplinary and collaborative, with links to experimental and theoretical groups and the broader WISE network.
Eligibility: a Master’s degree in Chemistry, Applied Physics, Materials Science, or a related field, or equivalent coursework/knowledge. Experience with machine learning potentials, Hamiltonian learning, or molecular dynamics is an advantage. Strong problem-solving, teamwork, and communication skills are valued.
Funding: PhD employment for four years full-time equivalent, initially one year and renewable; possible extension up to five years depending on teaching and departmental duties. Starting salary is SEK 35,300/month, with increases during the doctoral period. The position is connected to the WISE program and includes the WISE Research School.
Deadline: 25 September 2026. Apply through the university’s online portal using the “Apply” button.
Funding details
PhD employment at Linköping University for four years full-time equivalent, initially for one year and then renewable based on progress; may extend up to five years depending on teaching and departmental duties. Starting salary is SEK 35,300 per month, with increases after 30% and 60% of doctoral studies. The position is connected to the WISE program and includes participation in the WISE Research School.
What's required
Applicants must have a Master’s degree in Chemistry, Applied Physics, Materials Science, or a related field, or equivalent knowledge corresponding to at least 240 credits with at least 60 advanced credits in a relevant area. Strong interest in interdisciplinary research at the interface of chemistry, physics, materials, and energy technologies is expected. Experience with machine learning potentials, Hamiltonian learning, or molecular dynamics simulations of materials is an advantage. Strong problem-solving skills, ability to work in a multidisciplinary and international team, excellent communication abilities, and good personal suitability are emphasized.
How to apply
Apply via the university’s online application portal using the “Apply” button on the job page. Submit the application and required documents before 25 September 2026. Late applications will not be considered.
More information can be found here
Ask ApplyKite AI
How do I apply for this?
Sign in for free to reveal details, requirements, and source links.