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of this project will bring forward the integration of novel methods at the intersection of advanced control, optimization, manufacturing science, robotics, and machine learning. The doctoral student position we
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part of the Therapeutic Modalities function, where the discovery of chemical starting points for further optimization in medicinal chemistry is a primary focus. We closely collaborate with our
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develop and run the weather and climate model ICON. We are seeking a High Performance Computing (HPC) software engineer to further develop and optimize the ICON model (80-100%). Project background In order
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on the spectroscopic elucidation of transport properties in doped organic thin films. The main tools will include film preparation, optimization of doping protocols, electrical conductivity measurements, temperature
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) for engineering systems. Our research covers surrogate modeling, reliability analysis, sensitivity analysis, optimization under uncertainty, and Bayesian calibration. We are known for developing the UQLab software
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with solid-state fermentation (SSF) using filamentous fungi to optimize nutrient profiles in food. The goal is to develop predictive models in fermentation processes to achieve enhanced nutritional
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-relevant research challenges such as controlling high precision production machines, optimizing machine tools, or conceiving sustainable manufacturing processes. This position is envisaged to be part of
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. Additional familiarity with building your own embedded sensing sensing setup is a strong plus (e.g., for EMG/ECG/EOG or other differential or regular sensors). An optimal background for the project would be
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the Multiphase Fluid Dyamics group (Prof. Supponen) has a focus on modelling and high-speed imaging to optimize bubble generation and control, and the resulting particle dynamics and interaction with cells using
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experience in developing software for scientific applications, data analysis, or real-time systems is desirable. Experience with parallel computing and optimization techniques for handling large datasets