Job offer

The position is to be filled for a limited period of 36 months. Payment will be made according to pay group 13 TV-L Wissenschaft. The position is suitable for part-time employment.

Germany is undergoing profound demographic change, characterised by an ageing population and declining birth rates. This development is accompanied by an increase in age-related chronic diseases - and thus a significant growth in the need for long-term care. The increase in cognitive impairments such as mild cognitive impairment and dementia, which require a high level of care, is particularly serious. At the same time, the shortage of nursing staff is reaching alarming proportions. In view of these challenges, innovative solutions are needed.

The BehAIve project will make a promising contribution in this area. It aims to develop an AI-supported, situation-adaptive system to support older people in their everyday lives or geriatric patients during hospital stays. Continuous monitoring using sensors will analyse subjects' behaviour, identify problematic behaviour and offer targeted support where necessary. This can significantly improve the quality of life of people with cognitive impairments or geriatric patients - while also reducing the workload of nursing staff. The sub-project 'Scalable Systems for multimodal Data Analysis' deals with the development and implementation of methods for automatic knowledge extraction, multimodal behaviour analysis and language-based interaction.

For more details about the overarching research questions, specific topics approached by this position in the sub-project 'Scalable Systems for multimodal Data Analysis', and contact persons, please visit https://datascience.uni-greifswald.de/forschung/behaive/., The research assistant in the project 'Scalable Systems for multimodal Data Analysis' will have the following responsibilities:

• Development of scalable systems for automatic knowledge extraction and learning of planning models from textual data
• Development of scalable systems for language-based interaction
• Development of scalable systems for activity and situation recognition from sensor data
• Development of multimodal sensor systems for the recording of human behaviour
• Publication of the obtained results in scientific journals and conferences

We invite applications from highly motivated candidates with above-average qualifications, passion for and experience in research, and the willingness to actively participate in an interdisciplinary and international research environment. Successful applicants should have:

• University degree (master's or comparable) in artificial intelligence, data science or another related discipline, by the time employment starts
• Practical experience in machine learning, especially deep learning and its practical application in the domain of sensor analysis
• Practical experience with multimodal sensor systems
• Practical experience in the field of natural language processing, especially large language models, prompt engineering, finetuning, retrieval augmented generation (RAG), and GraphRAG approaches
• Hands on experience with Python
• Excellent English language skills, and
• Motivation to join an interdisciplinary and international research environment., Master Degree or equivalent