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Örebro University is offering a fully funded PhD position in Computer Science, focusing on tractable quantum machine learning. This opportunity is based at the School of Science and Technology and is designed for candidates interested in the intersection of classical and quantum approaches to efficient inference in machine learning. The project aims to explore the foundations of tractable probabilistic models—those supporting efficient and exact reasoning—and to investigate their emerging connections to quantum machine learning. The research will delve into how principles such as structured computation and controlled expressivity, which make classical models tractable, can be extended or reinterpreted within a quantum information theoretic framework. The ultimate goal is to uncover unifying concepts that link classical and quantum models, paving the way for new, expressive, and computationally efficient machine learning paradigms. The doctoral programme consists of both coursework and an independent research project, culminating in a doctoral thesis and the award of a PhD in Computer Science. The programme is structured as a full-time, four-year commitment (240 ECTS credits), with a clear introduction to doctoral studies, active participation in scientific seminars, and opportunities for networking and career development. Doctoral students benefit from a supportive academic environment and are encouraged to engage in conceptual work, prototype algorithm development, and contribute to the advancement of probabilistic and quantum AI. The position comes with a full-time doctoral studentship, guaranteeing employment for the duration of the programme (subject to satisfactory progress). The initial monthly salary is SEK 32,300, and the studentship is tailored for doctoral candidates. Örebro University fosters an inclusive and respectful work environment, actively promoting equal opportunities and gender equality. Applicants must meet both general and specific entry requirements. General requirements include a second-cycle qualification (Master's degree or equivalent), with at least 240 ECTS credits, including 60 credits at the second-cycle level. Specific requirements are a Master of Science in Engineering or a one-year Master's degree in computer science or related subjects, or at least 120 credits including an independent project in a relevant field. Equivalent qualifications from Sweden or abroad are accepted. The ideal candidate will have a strong background in probabilistic AI, linear algebra, and machine learning, with experience in quantum machine learning or tractable probabilistic models considered a significant advantage. Strong problem-solving, critical analysis, cooperative, and communication skills are essential. Fluency in spoken and written English is required; knowledge of Swedish is not necessary. Prior research experience, such as publications or involvement in scientific projects, is beneficial. To apply, candidates should submit an online application including a description of research interests, CV, independent project/thesis, proof of meeting entry requirements, and other relevant documents. Documents must be in Swedish, Danish, Norwegian, or English, or translated by an authorised translator. The application deadline is January 12, 2026. For further information, contact Dr. Pedro Zuidberg Dos Martires ([email protected]) for academic queries or Prof. Martin Magnusson ([email protected]) for administrative matters. More details and the application portal are available at the university's career site. This position is ideal for candidates passionate about conceptual research, abstract modeling, and the development of prototype algorithms in the rapidly evolving field of quantum machine learning.

Örebro University invites applications for a fully funded PhD position in Computer Science, focusing on the development of efficient, neuro-inspired methods for machine learning. The successful candidate will join the Machine Perception and Interaction Lab, a multidisciplinary research group at the intersection of artificial intelligence, robotics, machine learning, and human-robot interaction. The project aims to address the computational challenges of modern machine learning by drawing inspiration from biological neural systems, which excel at complex tasks under strict energy constraints. By integrating principles such as structural organization, recurrence, and randomness, the research seeks to create lightweight machine learning models that maintain high performance while dramatically reducing computational requirements. The doctoral project will explore how structured prior knowledge, memory of past inputs, and randomized representations can be combined to build models suitable for resource-constrained devices. Applications include long-term forecasting of dynamical systems and processing biomedical signals from wearable devices. The research will contribute both theoretical insights and practical advances, with the goal of developing a new framework for efficient machine learning that remains competitive with state-of-the-art approaches. The PhD programme consists of coursework and an independent research project culminating in a doctoral thesis. It spans four years of full-time study (240 ECTS credits) and includes a tailored seminar series, introduction to doctoral programme rules, and opportunities for networking and career support. The position is linked to a full-time doctoral studentship, guaranteeing employment for the duration of the programme (subject to satisfactory progress), with an initial monthly salary of SEK 32,300. Entry requirements include a second-cycle qualification (Master's degree or equivalent), with at least 240 ECTS credits, including 60 at the second-cycle level. Specific requirements are a Master of Science in Engineering or a one-year Master's degree in computer science or related subjects, or equivalent international qualifications. Candidates should demonstrate strong problem-solving, critical analysis, and communication skills. Fluency in spoken and written English is essential; knowledge of Swedish is not required. Merits include coursework, thesis, or publications in digital signal processing, electrical engineering, computer vision, machine learning, artificial intelligence, cognitive science, or robotics. Örebro University values diversity, equal opportunities, and a collaborative work environment. The application is made online and must include a description of research interests, CV, proof of meeting entry requirements, independent project, and other relevant documents. Documents should be in Swedish, Danish, Norwegian, or English; translations are required for other languages. The application deadline is 16 January 2026. For further information, contact Dr. Denis Kleyko ([email protected]), Prof. Amy Loutfi ([email protected]), or Prof. Martin Magnusson ([email protected]). More details and the application portal are available on the university’s career page.

Örebro University invites applications for up to two doctoral studentships in Computer Science, with a research focus on neuro-inspired computing. These positions are affiliated with the Machine Perception and Interaction Lab, a multidisciplinary group working at the intersection of artificial intelligence, robotics, machine learning, and human-robot interaction. The project centers on unconventional computing, specifically the development of novel neuro-inspired algorithms and their hardware realizations, aiming to make future intelligent systems more efficient and powerful. Current intelligent systems require massive datasets and large-scale computing infrastructure, limiting their accessibility and deployment. In contrast, biological brains achieve remarkable intelligence with minimal energy consumption. Drawing inspiration from these biological principles, the project seeks to rethink how intelligent systems represent information, perform computations, and physically implement algorithms. A key research direction involves high-dimensional neural representations, which offer robustness and powerful encodings for continual learning and reasoning. These representations are compatible with emerging hardware such as neuromorphic chips and in-memory computing devices. Doctoral students will develop frameworks connecting new learning algorithms with their physical implementation, targeting advanced computing hardware. The research will be evaluated in real-world domains like signal processing and combinatorial optimization, where efficient solutions can have significant societal and industrial impact. This is an opportunity to work at the frontier of artificial intelligence, computational neuroscience, neuro-inspired computing, and hardware technologies. The doctoral programme consists of courses and an independent research project culminating in a doctoral thesis. It comprises 240 ECTS credits over four years of full-time study. Students benefit from a tailored seminar series, introduction to programme rules, and networking and career support. The studentship guarantees full-time employment for the duration of the programme, with an initial salary of SEK 32,300 per month, subject to satisfactory progress. Entry requirements include a second-cycle qualification (Master's degree or equivalent), or completion of at least 240 ECTS credits (with at least 60 ECTS at the second-cycle level), or substantially equivalent knowledge. Specific requirements are a Master of Science in Engineering or a one-year Master's degree in computer science or related subjects, or at least 120 credits including an independent project in a relevant field. Candidates should demonstrate strong problem-solving, critical analysis, cooperative and communicative skills. Fluent English is essential; Swedish is not required. Merits include coursework, thesis, or publications in digital signal processing, electrical engineering, computer vision, machine learning, artificial intelligence, cognitive science, neuromorphic computing, or robotics. Örebro University values diversity, equal opportunities, and a work environment characterized by openness, trust, and respect. Application is online; required documents include a description of research interests, CV, proof of meeting entry requirements, independent project, and other relevant certificates. Documents must be in Swedish, Danish, Norwegian, or English; translations are required for other languages. The application deadline is 16 January 2026. For more information, contact Dr. Denis Kleyko ([email protected]), Prof. Amy Loutfi ([email protected]), or Prof. Martin Magnusson ([email protected]). For further details and to apply, visit the university’s career page or the direct application link provided.