Ida-Maria Sintorn

Uppsala University is recruiting a PhD student in computerized image processing and physics-informed machine learning for green hydrogen production . The project sits at the intersection of computer science , machine learning , computer vision , physics-informed modeling , materials science , and mathematics , with a strong application focus on accelerating Sweden’s transition to green hydrogen. The research will study proton exchange membrane water electrolyzers (PEMWE) and thermally sprayed titanium layers used for corrosion protection. The PhD student will develop automated pipelines for X-ray computed tomography (XCT) image segmentation and analysis, extract physically meaningful microstructural descriptors, build probabilistic surrogate models and a digital twin linking microstructure to electrochemical performance, and use Bayesian experimental design and process optimization to guide manufacturing parameters. The work is supervised by Ida-Maria Sintorn and Jens Sjölund at the Department of Information Technology, Uppsala University, in collaboration with Alleima and Sandvik . Eligible backgrounds include engineering physics, electrical engineering, image processing, computer vision, AI, machine learning, data science, computer science, and applied mathematics, or equivalent qualifications. Strong programming skills, preferably in Python, excellent study results, and good English communication skills are expected. Experience in image analysis, deep learning, optimization, numerical linear algebra, visualization, and software engineering is considered an advantage. The position is a temporary full-time PhD employment at Uppsala University. The employment may include up to 20% departmental duties such as teaching and administration. The starting date is 1 September 2026 or as agreed. The application deadline is 7 May 2026 . To apply, prepare a cover letter in English, a CV, degree documents and transcripts, thesis or scientific writing samples, publications, and reference details. Submit the application through Uppsala University’s recruitment system.

Uppsala University is advertising a PhD position in computerized image processing and physics-informed machine learning for green hydrogen production at the Department of Information Technology in Sweden. The project sits at the intersection of computer science , machine learning , computer vision , 3D image processing , physics-informed machine learning , materials science , and chemical engineering , with a focus on improving proton exchange membrane water electrolyzers (PEMWE) for green hydrogen production. The research will develop automated pipelines for segmenting and analysing X-ray computed tomography (XCT) images of thermally sprayed titanium layers, extract physically meaningful microstructural descriptors, build probabilistic surrogate models and digital twins, and use Bayesian experimental design and Bayesian optimization to guide process optimization. The work is supervised by Ida-Maria Sintorn (Professor in digital image processing) and Jens Sjölund (Assistant Professor in AI), in collaboration with Alleima and Sandvik . Eligibility highlights include a relevant Master’s degree or equivalent credits in engineering physics, electrical engineering, image processing, computer vision, AI, machine learning, data science, computer science, or applied mathematics. Strong programming skills in Python, excellent academic results, good English communication, and a creative, structured problem-solving style are requested. Experience in image analysis, deep learning, optimization, numerical linear algebra, statistical machine learning, visualization, and software engineering is considered an advantage. The position is a temporary full-time PhD employment (100%), with doctoral studies as the main duty and up to 20% departmental duties such as teaching and administration. The application deadline is 7 May 2026 , and the expected start date is 1 September 2026 or as agreed. Applicants must submit a cover letter, CV, degree documents, thesis or draft, relevant publications, and reference contacts through Uppsala University’s recruitment system.