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in large language models for healthcare research, with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in sustainable
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increasingly rely on data-driven models to extract, represent, and interpret information from complex and evolving environments. Traditional machine learning approaches, as well as many classical signal
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transport in solid‑state polymer electrolytes for Li‑ion batteries. The research will focus on developing new and computationally efficient strategies for multiscale modelling, enabling systematic studies
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both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates
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microstructural descriptors that are physically meaningful and predictive. Probabilistic surrogate modelling and digital twin construction. The extracted microstructural descriptors will be used to learn a
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impurities (e.g. Ca²⁺, Mg²⁺, Al³⁺, F⁻, Fe³⁺) while minimizing co-precipitation of valuable transition metals (Ni, Co, Mn). The project combines experimental studies, thermodynamic/speciation modeling, and
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. At the Division of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and
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focuses on leveraging zebrafish as a model organism to develop and optimize genetic tools through a directed evolution pipeline, with significant therapeutic and industrial applications. Key
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, comparing it with other drug classes and countries. Applying a mixed-method approach, the project integrates qualitative case studies with quantitative Social Network Analysis, simulations (Agent-Based Models
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, electrode fabrication, electrochemical testing, and data-driven modelling to correlate synthesis parameters with material performance. The doctoral student is expected to actively participate in project