Jens Sjölund

Uppsala University is seeking a postdoctoral researcher in unsupervised machine learning for battery timeseries data, supervised by Assistant Professors Jens Sjölund and Leiting Zhang. The position is part of the COMPEL initiative, focusing on the electrification of the transport system and battery development. The research aims to develop interpretable machine learning methods for extracting dynamical models of battery degradation from multimodal timeseries data, including high-frequency acoustic emission and electrochemical measurements. The project emphasizes moving beyond black-box prediction by learning low-dimensional latent representations that capture underlying physical processes in batteries, such as particle fracture and gas evolution. Methodological components may include self-supervised temporal representation learning, switching state-space models, and neural ODE-based latent dynamics. The research will contribute to mechanistic insight and enable interpretable battery health diagnostics and prognostics. The position is based at the Division of Systems and Control, Department of Information Technology, Uppsala University, which is known for its interdisciplinary research in control theory, machine learning, optimization, and network science, with applications in energy systems, biomedical systems, and more. Applicants must have a PhD in machine learning, automatic control, system identification, signal processing, applied mathematics, battery systems, or a related field, with a strong technical background in relevant areas. Experience in programming, a record of publication in top venues, and proficiency in English are required. The position is fully funded for two years, with a competitive salary and the possibility of up to 20% teaching. The application deadline is February 2, 2026, and the expected start date is March 1, 2026, or as agreed. Applications should be submitted through Uppsala University's recruitment system and include a CV, grade documents, publication list, selected publications, research statement, proposal for future activities, and references. This opportunity is ideal for candidates interested in interdisciplinary research at the intersection of machine learning, battery technology, and dynamical systems, offering a collaborative and international environment at one of Sweden's leading universities.

Uppsala University is offering a postdoctoral position in machine learning for battery timeseries data at the Department of Information Technology. The research is part of the COMPEL initiative, a strategic Swedish government program focused on advancing battery development and the electrification of the transport sector. The project aims to develop unsupervised machine learning methods for extracting dynamical models of battery degradation from multimodal timeseries data, emphasizing interpretability and mechanistic insight. The data includes high-frequency acoustic emission and electrochemical measurements from operating batteries, targeting complex processes such as particle fracture and gas evolution. The research will involve self-supervised temporal representation learning, switching state-space models, and neural ODE-based latent dynamics to analyze large volumes of unlabeled data. The goal is to create an integrated framework for interpretable battery health diagnostics and prognostics, advancing the understanding of battery aging and enabling real-time monitoring. The project is highly interdisciplinary, integrating expertise from machine learning, control theory, optimization, and network science, and is supervised by Assistant Professors Jens Sjölund (machine learning) and Leiting Zhang (battery sensing). Applicants must have a PhD in machine learning, automatic control, system identification, signal processing, applied mathematics, battery systems, or a related field, with strong technical skills and a record of publications in top venues. Proficiency in programming and excellent English are required. The position is full-time for two years, with a fixed salary, and may include up to 20% teaching. The application deadline is February 2, 2026, and the expected start date is March 1, 2026. For more information, contact the supervisors at [email protected] and [email protected]. To apply, submit your application through Uppsala University's recruitment system, including a CV, grade documents, publication list, selected publications, research statement, and references. Uppsala University offers a collaborative and international research environment, with strong support for interdisciplinary work and career development.

Uppsala University, a leading research institution in Sweden, is inviting applications for a postdoctoral position in unsupervised machine learning for battery timeseries data. The position is based at the Department of Information Technology, specifically within the Division of Systems and Control, which is renowned for its interdisciplinary research integrating control theory, machine learning, optimization, and network science. The research will focus on developing interpretable unsupervised machine learning methods to extract dynamical models of battery degradation from multimodal timeseries data, including high-frequency acoustic emission and electrochemical measurements. This project is part of the COMPEL initiative, a strategic Swedish government program aimed at advancing the electrification of the transport sector and battery development. The successful candidate will join a vibrant, international research environment and collaborate with experts in machine learning and battery sensing, including Assistant Professors Jens Sjölund and Leiting Zhang. The research aims to move beyond black-box prediction by learning low-dimensional latent representations that capture underlying physical processes in batteries, enabling new diagnostic capabilities for real-time battery monitoring. Applicants should have a PhD in machine learning, automatic control, system identification, signal processing, applied mathematics, battery systems, or a related field. Strong technical skills in machine learning, optimization, probabilistic modelling, and programming are required, along with a proven publication record and proficiency in English. The position is fully funded for two years, with a competitive salary and the possibility of teaching up to 20% of the time. The application deadline is February 2, 2026, and the expected start date is March 1, 2026, or as agreed. To apply, candidates must submit a CV, grade documents, publication list, up to five selected publications, a research statement, a proposal for future activities, and contact information for two references through Uppsala University's recruitment system. For further information, contact Assistant Professors Jens Sjölund ([email protected]) or Leiting Zhang ([email protected]). This is an excellent opportunity for researchers interested in interdisciplinary work at the intersection of machine learning, battery technology, and data-driven modelling.

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.