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imaging using deep learning. You will study the water imbibition in hierarchically porous Si‑based material systems across multiple length and time scales. These systems can manipulate fluid transport
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, Bioengineering, or a related field Strong interest in translational endocrinology and digital health technologies Basic programming or data science skills (R, Python) and interest in wearable data analysis
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change, market dynamics, and daily grid variations. These factors contribute to heightened structural and control complexity, along with multiple layers of uncertainty. In this context, Hybrid Power Plants
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, or modelling. Familiarity with computational tools (Matlab, Python, or finite element analysis). Analytical thinking and enthusiasm for interdisciplinary research. Ability to work independently and as part of a
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. Hands-on experience in fermentation, microbial cultivation, and analytical techniques (HPLC/GC). Familiarity with bioprocess modelling and simulation tools (e.g., Aspen Plus, MATLAB, or Python), TEA, and
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multiple spatial and temporal scales. By adopting a multi-scale perspective, the project aims to significantly improve our ability to observe and quantify flood-related hazards. The main study area will be
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requirements: Excellent analytical skills Strong verbal and written communication skills Desirable: Strong proficiency in optimising and modelling power and energy systems Programming skills in Python and Julia
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neuroscienceanalyses to MRI data from multiple species , the project aims to study the evolutionary basis of human cognition —including language, working memory, theory-of-mind , and the specialized brain networks
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candidate would have experience with computational modelling and control of dynamical systems. Other useful skills include scientific programming (e.g., Python or Matlab), control system design, and
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monitoring. To address these limitations, the proposed research will integrate UAV-based imaging, satellite remote sensing, and AI-supported classification workflows to quantify lichen distribution at multiple