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or observing programmes Machine learning in physical science, especially with transformer models Jax, PyTorch, and/or Julia; with probabilistic programming languages; or with high-dimensional optimization and
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outcomes under different market design scenarios. The research will combine machine learning, stochastic optimization, and agent-based modelling with behavioural experiments. Case studies from emerging
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Description Scope of duties (full-time, on-site post-doctoral position): a. Synthesis of metallic nanostructures (both bare and surface-functionalized), b. Optimization of materials intended for the fabrication
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in clinically relevant environments. Key work assignments include: Design, fabrication, and optimization of high-performance plasmonic nanostructures and SERS substrates for sensing in complex
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transport system. Our research portfolio is unique and covers all modes of transport and contributes to environmentally friendly process technology and sustainable energy supply. Through collaboration with
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biomedical and biological data, including developing and optimizing models to predict disease progression and create realistic patient profiles; Building and optimizing pipelines for pre-processing and
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, the establishment and optimization of behavioral assays under controlled oxygen conditions, image‑based analyses, and quantitative data processing and interpretation. The role also includes active participation in
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, tested, analysed, disassembled, components washed, and remanufactured. The established process is optimized considering the chemical process solutions during electrode washing. Finally, the process is
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different scales, with the aim to improve our understanding ofthe role of snow in the climate system. Within our Snow Physics group, we are looking for a Postdoc in "Improving understanding & model
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future of the C4 grasslands of the world”. We will develop a novel approach for simulating C4 grasslands in interaction with C3 grasslands and trees based on Eco-Evolutionary Optimality (EEO) within