Applied Researcher in Data-driven Nutrition

About the role

Fraunhofer-Chalmers Centre (FCC) is a leading research centre in industrial mathematics, modelling, simulation, optimisation, and data analytics. We operate at the interface between academic research and industrial needs, often in interdisciplinary projects where mathematics, data, and domain expertise meet. Together with the nutrition research group led by Professor Rikard Landberg at Chalmers University of Technology, we conduct advanced research in data-driven nutrition, health, and food science. With large-scale diet and health data, omics data, biomarkers, digital food and health services, we establish predictive models for evaluation of the role of diet in health and disease and establish personalized dietary strategies for more effective disease prevention. In many cases, the work involves time series data, dynamic processes and phenomena, where both methods and interpretation must account for temporal dynamics. Data typically comes from intervention studies conducted in Gothenburg and or from large cohorts and biobanks from international collaborators. To secure and further develop this important collaboration, we are now recruiting an applied researcher with a strong quantitative profile and interest in nutrition, health, or medicine. Your role As an applied researcher in this area, you will: Act as a key person in the collaboration between FCC and the nutrition research group at Chalmers. Lead and conduct research projects in data-driven nutrition, such as:analysis of time series data and dynamic processes, where signals and responses evolve over time. statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome, etc.) development of predictive dynamical models and digital decision-support tools for nutrition and health method development in causal inference, integration of heterogeneous data sources, uncertainty quantification Work with a wide range of data types, for example dietary records, biomarkers, omics data, registry data, and sensor data such as CGM measurements (continuous glucose monitoring), activity trackers, and other wearable sensors. Serve as a bridge between domain researchers (nutrition, medicine, food science) and quantitative experts (mathematics, statistics, AI&ML, systems engineering). Supervise, collaborate, and support PhD students and postdocs involved in joint projects. Your profile We are looking for someone who: Holds a PhD in Applied Mathematics, Mathematical Statistics, Automatic Control, Signal Processing, Systems Engineering, Data Science, or a related field. Has experience in data-driven research, preferably related to biomedicine, nutrition, epidemiology, food science, or public health. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible analysis workflows. Enjoys working in interdisciplinary environments and is curious about understanding biological/nutritional research questions in depth. It is meritorious if you also: Have experience working with cohort data, registry data, clinical studies, or omics data. Have previously worked in projects involving both academic and industrial/external partners. Have experience supervising PhD students, postdocs, or junior researchers. Can communicate in both Swedish and English; excellent English is required.

Benefits

Additional Information

We are seeking an ambitious applied researcher who wants to work at the forefront of data-driven nutrition and health. This is a unique opportunity to bridge mathematics, statistics, AI, and biomedical research in a highly interdisciplinary research environment.

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