Research assistant in Trustworthy Machine Learning for Elderly Care

Malmö University; Malmö kommun; Sur place; SEK 89 280 - 133 920; Temps partiel

Résumé du poste

Malmö University invites applications for a Research Assistant in Trustworthy AI for Elderly Care located in Malmö. The role supports a project developing robust ML frameworks for decision-support, focusing on transparency and interpretability across multimodal data.

You will implement and evaluate models, conduct literature reviews, contribute to publications, and work onsite on campus. Requires a Master’s degree or equivalent and strong Python and ML framework skills.

Qualifications

• Advanced degree or equivalent expertise.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Strong English writing and communication skills.
• Ability to work independently and in interdisciplinary teams.
• Valid work permit or right to work in Sweden.
• On-site campus-based position.

Responsabilités

• Conduct literature reviews on self-supervised learning, domain adaptation, multi-modal data fusion, and XAI.
• Develop, train, and evaluate advanced ML models on longitudinal datasets.
• Develop domain adaptation methods to improve robustness and transferability.
• Apply XAI techniques to extract interpretable insights.
• Preprocess, synchronize, manage, and analyze multi-modal datasets.
• Contribute to publications, documentation, and dissemination.

Connaissances

Python

PyTorch

TensorFlow

Scikit-learn

English writing/communication

Independent work

Team collaboration

Formation

Master's degree or equivalent

Description du poste

Research Assistant in Trustworthy AI for Elderly Care

Location: Malmo

Reference number: P 2026/1471

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 Requirements

A research assistant must have a completed degree on an advanced level or equivalent expertise.

• 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 connection.

Assessment Criteria

• 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 or strong interest in XAI, model interpretability, and trustworthy machine learning.
• Experience in processing, engineering, and analyzing multi-modal data, time-series, or longitudinal tabular datasets.
• Experience with data preprocessing pipeline development and privacy-preserving analytics.
• Previous experience contributing to academic research environments and drafting scientific papers.

Terms of Employment

Starts: 1 September 2026

Form of employment: Fixed-term employment for 3.5 months

Scope of the employment: 30%

Salary: Individual salary setting

Swedish is the primary language at Malmö University, and it is hoped that non‑Swedish speaking staff will acquire enough Swedish language skills to participate in teaching, research, and common daily work in the department.