Postdoc in AI-Driven Materials Discovery
Chalmers Tekniska Högskola AktiebolagSammanfattning
Join the Division of Chemical Physics at Chalmers University of Technology as a postdoctoral researcher focused on developing AI methods for materials discovery and sustainable energy technologies. This role involves creating an autonomous materials-discovery framework and collaborating with interdisciplinary teams in a dynamic research environment in Gothenburg. The position is temporary for two years, with potential for extension, requiring physical presence throughout the employment.Jobbet i korthet
Anställningstyp
tillsvidareanstallning
Arbetstid
heltid
Förmåner
Dynamic and inspiring working environment in Gothenburg.Employee benefits as part of Chalmers University.Opportunities for professional development in research and education.Support for settling in, including Swedish language courses.
Ansök senast: 2026-07-17
Publicerad: 2026-06-18
Beskrivning
Join us at the Division of Chemical Physics, Department of Physics and Astronomy, and contribute to the development of next-generation artificial intelligence methods for materials discovery and sustainable energy technologies.
About us
The Division of Chemical Physics conducts internationally recognized research at the interface of physics, chemistry, materials science, and data science. Our research spans computational materials design, catalysis, energy materials, machine learning, and artificial intelligence. We offer a collaborative and international research environment with close interactions between faculty, postdoctoral researchers, PhD students, and external partners from academia and industry.
The Department of Physics and Astronomy is one of the largest departments at Chalmers University of Technology, with broad activities ranging from fundamental physics to applied research addressing societal challenges in energy, sustainability, and digitalization.
About the research project
The project aims to develop novel AI and machine-learning methods for accelerated materials discovery. The focus is on combining generative AI, active learning, first-principles simulations, and machine-learning potentials to identify new multicomponent optoelectronic materials for energy-conversion applications.
The successful candidate will contribute to the development of an autonomous materials-discovery framework capable of navigating extremely large compositional spaces while simultaneously optimizing multiple target properties such as optical performance, stability, and sustainability.
Who we are looking for
We seek a highly motivated and independent researcher with a strong interest in artificial intelligence, computational materials science, and scientific software development.
Mandatory qualifications
A doctoral degree in Physics, Materials Science, Chemistry, Chemical Engineering, Computer Science, Applied Mathematics, or a closely related field, or an equivalent foreign degree. The degree must be awarded no later than the time the employment decision is made.
Strong written and verbal communication skills in English.
Experience in machine learning, scientific computing, computational physics, computational chemistry, or related fields.
Experience with Python programming and modern scientific software development.
Ability to work independently while contributing effectively to collaborative research projects.
Strong analytical and problem-solving skills.
Good interpersonal skills and a demonstrated ability to collaborate in interdisciplinary teams.
You are expected to have some experience with teaching or supervision and to demonstrate strong potential for future development in both research and education.
Meritorious qualifications
It is highly meritorious if the doctoral degree has been obtained within the last three years prior to the application deadline.
Experience with machine learning for scientific applications.
Experience with deep learning frameworks such as PyTorch or TensorFlow.
Experience with atomistic simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials.
Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization, or reinforcement learning.
Experience with high-performance computing (HPC).
Experience supervising students or junior researchers.
What you will do
As a postdoctoral researcher, you will:
Develop machine-learning and AI methods for materials discovery.
Design and implement active-learning workflows for autonomous exploration of materials spaces.
Develop and evaluate generative AI models for inverse materials design.
Perform large-scale computational screening using first-principles calculations and machine-learning potentials.
Analyze structure-property relationships and extract scientific insights from AI models.
Publish research results in leading international journals and present findings at international conferences.
Contribute to the development of open and reproducible scientific software.
The position also includes:
Supervision of master's students and, to some extent, PhD students.
Opportunities to contribute to teaching at undergraduate and master's levels.
Collaboration with national and international academic and industrial partners.
Contract terms
The position is a temporary full-time employment for two years with the possibility of a one-year extension.
The position requires physical presence throughout the entire employment. A valid residence permit must be presented by the start date, otherwise the offer may be withdrawn.
What we offer
As a postdoc at Chalmers, you are an employee and enjoy all employee benefits. Read more about working at Chalmers and our benefits for employees.
A dynamic and inspiring working environment in the coastal city of Gothenburg.
Read more about Sweden's generous parental leave, subsidized day care, free schools, healthcare etc at Move To Gothenburg.
Chalmers is dedicated to improving gender balance and actively works with equality projects, such as the GENIE Initiative for gender equality and excellence. We celebrate diversity and consider equality and inclusion as fundamental aspects of all our activities.
If Swedish is not your native language, Chalmers offers Swedish courses to help you settle in.
Application procedure
The application should be written in English be attached as PDF-files, as below. Maximum size for each file is 40 MB. Please note that the system does not support Zip files.
CV
A comprehensive CV, including a complete list of publications.
Details of previous teaching and pedagogical experience.
Personal letter
A brief introduction about yourself.
A summary of your previous research fields and key research outcomes.
An outline of your future goals and research focus.
Use the button at the foot of the page to reach the application form.
A background check may be conducted as part of the application process.
Please note: The applicant is responsible for ensuring that the application is complete. Incomplete applications and applications sent by email will not be considered. Contact details to references will be requested after the interview.
We welcome your application no later than 17 July
For questions please contact:
Anders Hellman
Professor
anders.hellman@chalmers.se
About us
The Division of Chemical Physics conducts internationally recognized research at the interface of physics, chemistry, materials science, and data science. Our research spans computational materials design, catalysis, energy materials, machine learning, and artificial intelligence. We offer a collaborative and international research environment with close interactions between faculty, postdoctoral researchers, PhD students, and external partners from academia and industry.
The Department of Physics and Astronomy is one of the largest departments at Chalmers University of Technology, with broad activities ranging from fundamental physics to applied research addressing societal challenges in energy, sustainability, and digitalization.
About the research project
The project aims to develop novel AI and machine-learning methods for accelerated materials discovery. The focus is on combining generative AI, active learning, first-principles simulations, and machine-learning potentials to identify new multicomponent optoelectronic materials for energy-conversion applications.
The successful candidate will contribute to the development of an autonomous materials-discovery framework capable of navigating extremely large compositional spaces while simultaneously optimizing multiple target properties such as optical performance, stability, and sustainability.
Who we are looking for
We seek a highly motivated and independent researcher with a strong interest in artificial intelligence, computational materials science, and scientific software development.
Mandatory qualifications
A doctoral degree in Physics, Materials Science, Chemistry, Chemical Engineering, Computer Science, Applied Mathematics, or a closely related field, or an equivalent foreign degree. The degree must be awarded no later than the time the employment decision is made.
Strong written and verbal communication skills in English.
Experience in machine learning, scientific computing, computational physics, computational chemistry, or related fields.
Experience with Python programming and modern scientific software development.
Ability to work independently while contributing effectively to collaborative research projects.
Strong analytical and problem-solving skills.
Good interpersonal skills and a demonstrated ability to collaborate in interdisciplinary teams.
You are expected to have some experience with teaching or supervision and to demonstrate strong potential for future development in both research and education.
Meritorious qualifications
It is highly meritorious if the doctoral degree has been obtained within the last three years prior to the application deadline.
Experience with machine learning for scientific applications.
Experience with deep learning frameworks such as PyTorch or TensorFlow.
Experience with atomistic simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials.
Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization, or reinforcement learning.
Experience with high-performance computing (HPC).
Experience supervising students or junior researchers.
What you will do
As a postdoctoral researcher, you will:
Develop machine-learning and AI methods for materials discovery.
Design and implement active-learning workflows for autonomous exploration of materials spaces.
Develop and evaluate generative AI models for inverse materials design.
Perform large-scale computational screening using first-principles calculations and machine-learning potentials.
Analyze structure-property relationships and extract scientific insights from AI models.
Publish research results in leading international journals and present findings at international conferences.
Contribute to the development of open and reproducible scientific software.
The position also includes:
Supervision of master's students and, to some extent, PhD students.
Opportunities to contribute to teaching at undergraduate and master's levels.
Collaboration with national and international academic and industrial partners.
Contract terms
The position is a temporary full-time employment for two years with the possibility of a one-year extension.
The position requires physical presence throughout the entire employment. A valid residence permit must be presented by the start date, otherwise the offer may be withdrawn.
What we offer
As a postdoc at Chalmers, you are an employee and enjoy all employee benefits. Read more about working at Chalmers and our benefits for employees.
A dynamic and inspiring working environment in the coastal city of Gothenburg.
Read more about Sweden's generous parental leave, subsidized day care, free schools, healthcare etc at Move To Gothenburg.
Chalmers is dedicated to improving gender balance and actively works with equality projects, such as the GENIE Initiative for gender equality and excellence. We celebrate diversity and consider equality and inclusion as fundamental aspects of all our activities.
If Swedish is not your native language, Chalmers offers Swedish courses to help you settle in.
Application procedure
The application should be written in English be attached as PDF-files, as below. Maximum size for each file is 40 MB. Please note that the system does not support Zip files.
CV
A comprehensive CV, including a complete list of publications.
Details of previous teaching and pedagogical experience.
Personal letter
A brief introduction about yourself.
A summary of your previous research fields and key research outcomes.
An outline of your future goals and research focus.
Use the button at the foot of the page to reach the application form.
A background check may be conducted as part of the application process.
Please note: The applicant is responsible for ensuring that the application is complete. Incomplete applications and applications sent by email will not be considered. Contact details to references will be requested after the interview.
We welcome your application no later than 17 July
For questions please contact:
Anders Hellman
Professor
anders.hellman@chalmers.se
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Postdoc in AI-Driven Materials Discovery
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Chalmers Tekniska Högskola Aktiebolag
