The University of Augsburg’s ZD.B Chair of Embedded Intelligence for Health Care and Wellbeing positions itself at the intersection of modern Computer Science and Medicine. The chair pursues research in the field of computationally intelligent, ubiquitous sensing for knowledge-based monitoring of health-related activity, vital parameters, wellbeing, and contextual factors. The primary interest lies on robust multi-sensorial capturing, analysis, and interpretation of bio-signals such as cardio-, metabolic, or neuro-signals; moreover, modelling acoustic (from speech and non-speech) and visual (from face, gesture, and body) information with an integrated approach, in everyday (“in the wild”) environments, for mHealth and Affective Computing. For optimal individual user benefit and experience, this is complemented by socio-emotionally competent user modelling, feedback generation, interfaces, and applications for digital solutions of health state, fitness monitoring, and sports-related coaching. To best fulfil these goals, the chair develops novel algorithms in the fields of deep and general machine learning, and robust signal processing.