Doctoral student in Computer Science - Efficient Methods for ML
Örebro universitet / Högskolejobb / Örebro
2025-12-05
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hela Sverige Örebro University and the School of Science and Technology are looking for a doctoral student in Computer Science. The position is expected to conclude with a doctoral degree.
Start date: Spring 2026.
The doctoral student will be affiliated with the Machine Perception and Interaction Lab at Örebro University, which carries out multi-disciplinary research at the intersection of artificial intelligence, robotics, machine learning, and human-robot interaction.
Project description
The focus of the project is machine learning and specifically the development of novel neuro-inspired and computationally efficient methods.
Modern machine learning methods often depend on enormous datasets and extensive computing resources. This trend limits their scalability, increases their environmental footprint, and makes advanced machine learning accessible only to those with significant computational power. Meanwhile, biological neural systems, such as insect or human brains, operate under extreme energy constraints yet still handle complex tasks remarkably well. They achieve this by leveraging such computational principles as structural organization, recurrence (memory over time), and even randomness.
Integrating these biological principles into machine learning opens a path toward methods that require far less computation while still delivering strong processing capabilities. To make this possible, both theoretical and practical advances are needed: a deeper understanding of how learning systems can build compact and selective memories, how they can leverage prior knowledge to interpret new data efficiently, and how state-of-the-art architectures like transformers could incorporate these principles to better handle challenges such as long-range temporal dependencies, limited training data, and restricted computing budgets.
The goal of the project is to explore how structured prior knowledge, memories of past inputs, and randomized representations can be combined to create high-performing machine learning models that run even on resource-constrained devices. This work aims to produce a new framework for lightweight machine learning: methods that remain competitive with state-of-the-art models while dramatically reducing computations. The project will demonstrate its impact in demanding challenging domains such as long-term forecasting of dynamical systems and the processing of biomedical signals from wearable devices.
The programme, doctoral studentship, entry requirements and selection
To see the job advertisement in its entirety visit:
https://www.oru.se/english/career/available-positions/job/?jid=20250375Information
For more information about the programme and the doctoral studentship, please contact Dr. Denis Kleyko, e-mail:
denis.kleyko@oru.se and Prof. Amy Loutfi, e-mail:
amy.loutfi@oru.se. For administrative issues, please contact Prof. Martin Magnusson, e-mail:
martin.magnusson@oru.se.
At Örebro University, each member of staff is expected to be open to development and change; take responsibility for their work and performance; demonstrate a keen interest in collaboration and contribute to development; as well as to show respect for others by adopting a constructive and professional approach.
Örebro University actively pursues equal opportunities and gender equality as well as a work environment characterized by openness, trust and respect. The qualities that diversity adds to operations are highly valued.
Application to the programme and for the doctoral studentship
The application is made online. Click the button "Apply" to begin the application procedure.
For the application to be complete, the following electronic documents must be included:
• Description of research interests - describing your research interests, explaining why you are interested in this project, and why you would be a good fit for the position (1 page)
• CV
• Proof that you meet the general and specific entry requirements (e.g., copies of the original certificate and official transcript for Bachelor's and Master's degrees)
• Independent project (degree project)
• Other relevant documents, course and degree certificates verifying eligibility
As a main rule, application documents and attachments are to be written in Swedish, Danish, Norwegian, or English. Certificates and documents in other languages verifying your qualifications and experience must be translated by an authorised translator to Swedish or English. A list of authorised translators can be obtained from Kammarkollegiet (the Legal, Financial and Administrative Services Agency),
www.kammarkollegiet.se/engelska/start.When you apply for admission, you automatically also apply for a doctoral studentship.
More information for applicants can be found on the university's career page:
https://www.oru.se/english/career/available-positions/applicants-and-external-experts/The application deadline is 2026-01-16. We look forward to receiving your application!
The university declines any contact with advertisers or recruitment agencies in the recruitment process.
As directed by the National Archives of Sweden (Riksarkivet), one file copy of the application documents, excluding publications, is required to be deposited for a period of two years after the appointment decision has gained legal force.
Ersättning Lönesättning enligt lönestege.
Så ansöker du Sista dag att ansöka är 2026-01-16
Klicka på denna länk för att göra din ansökan Omfattning Detta är ett heltidsjobb.
Arbetsgivare Örebro Universitet (org.nr 202100-2924)
Arbetsplats Örebro universitet
Jobbnummer 9631641