Doctoral Student in Machine Learning for Integrated Resources Modelling
Join KTH to advance energy systems using machine learning.
Best for:Best for candidates with a strong background in energy technology and machine learning.
Overview
KTH Royal Institute of Technology is seeking a PhD student to enhance methodologies in energy systems and integrated resource assessment through machine learning. The successful candidate will develop workflows to explore uncertainties and scenario spaces in resource models, contributing to both developed and developing contexts. This role involves collaboration with government and stakeholders, requiring travel and participation in capacity development activities. The position is supervised by Francesco Gardumi, with a focus on independent research and teamwork.
What you'll do
- Develop machine learning workflows for integrated resource models.
- Explore uncertainties and scenario spaces in energy systems.
- Collaborate with external partners and stakeholders.
- Participate in capacity development activities.
- Engage in research that supports integrated resource planning.
Requirements
Degree required: Master's degree or equivalent in a relevant field
Degree offered: PhD
Language: English; equivalent to English B/6
Instruction: English
- Strong knowledge of energy technology and integrated resource systems
- Experience with data processing and machine learning techniques
- Ability to work independently and collaboratively
- Analytical skills for complex problem-solving
- Must have a second cycle degree or equivalent qualifications
- Open to candidates with equivalent knowledge from other countries
Funding & terms
How to apply
Deadline: 2026-10-10 (Europe/Stockholm)
Applications must be submitted through KTH's recruitment system. Ensure that your application is complete according to the provided instructions.
- Copies of diplomas and grades
- CV
- Application letter
- Representative publications or technical reports
Contact
Francesco Gardumi· Supervisor
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