Research assistant in Trustworthy Machine Learning for Elderly Care

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Testo originale dell'annuncio (in inglese)

Malmö university is an open, innovative and international university that contributes to societal development thanks to its dedicated and experienced staff. Here, teachers, researchers, and other staff members with diverse skills work together to deliver education and conduct research of the highest quality. All professional categories and roles are important. We welcome you to apply for a job with us! Malmö University is a workplace and an educational institution that strives for an open and inclusive approach, where gender equality and equal opportunities add value to our operations. Here you can read more about what it’s like to work at Malmö University: Work with us. For international applicants, read more here: Moving to Malmö and Sweden We are looking for a Research Assistant in Trustworthy AI for Elderly Care, at the Department of Computer Science and Media technology, Faculty of Technology and Society. Subject area The Research Assistant is to support a research project focused on developing advanced, trustworthy, and robust machine learning frameworks for complex decision-support systems. The project investigates how advanced machine learning methodologies can be integrated to analyze multimodal and heterogeneous datasets. A particular focus of this research is on developing models that are adaptable across different environments and populations, while ensuring the outcomes are transparent and clinically or practically interpretable. This interdisciplinary position integrates multimodal data fusion with advanced machine learning approaches, including self-supervised learning, domain adaptation, and explainable AI (XAI). Job description Conduct comprehensive literature reviews on self-supervised learning, domain adaptation, multi-modal data fusion, and XAI. Support the implementation, training, and evaluation of advanced machine learning models using complex and longitudinal datasets. Assist in developing domain adaptation methods to enhance model robustness and transferability across diverse settings. Apply XAI techniques to extract interpretable, reliable, and user-understandable insights from complex models. Assist in preprocessing, synchronizing, managing, and performing exploratory data analysis on multi-modal datasets. Contribute to scientific publications, technical documentation, and research dissemination activities. Qualification requirement Qualification requirements refer to requirements that must be met for an applicant to be considered for employment. A research assistant must have a completed a degree on advanced level or equivalent expertise. It is a general qualification requirement for teachers at Malmö University that the candidate is appropriate for and have the ability in general required to fully complete the duties of the relevant position. Additional qualification requirements for this position are: Strong programming and software development skills, with high proficiency in Python. Demonstrated practical experience with major machine learning and deep learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn). Strong communication and scientific writing skills in English. Ability to work independently and collaborate in interdisciplinary research teams. Applicants must have a valid work permit or legal right to work in Sweden. The position is campus-based and requires regular on-site presence; it is not a remote position. Assessment criteria Assessment criteria refer to criteria for assessing how well an applicant meets a certain qualification requirement and criteria that have otherwise been deemed significant for the position. The overall assessment includes proficiency in the specific field relevant to the position. For this position, the following will also be assessed: Experience with self-supervised learning, unsupervised learning, or representation learning algorithms. Knowledge or practical experience in domain adaptation, transfer learning, or covariate shift mitigation. Background