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ecology and biomonitoring, or related fields, with experience in image acquisition, large dataset processing, and statistical analysis. A passion for interdisciplinary research that bridges AI-driven
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biotechnological applications. Current proteomics approaches rely almost exclusively on positive ion mode, which leads to inefficient ionization of many acidic peptides. However, acidic and alkaline proteins are
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methods to enhance the understanding of the Greenland Ice Sheet, peripheral glaciers, and their contribution to sea level rise using finite element models such as the Ice-sheet and Sea-level System Model
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datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning). The goals are to develop new computational methods that allow the scientific inference
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will be given to research into (1) Hydrogen-related materials, (2) Battery materials, (3) Quantum materials, (4) Semiconducting materials, (5) Artificial intelligence materials, (6) Metal and inorganic
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I-2503 – PHD IN EXPLAINABLE AI FOR DATA-DRIVEN PHYSIOLOGICAL AND BEHAVIORAL MODELLING OF CAR DRIVERS
(e.g., InfoGAN, β-TCVAE, TopDis / Topological Disentanglement, Independent Component Analysis (ICA), Variational Autoencoders (VAEs), and matrix factorization techniques); · Experience with
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optical and chemical tools with classical genetic techniques and advanced image analysis to study cytoskeletal force dynamics quantitatively. A key focus is understanding how microtubules contribute
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for fabrication. In recent years, photoelectrochemically porosified SiC emerged as a promising technology to integrate optical elements (e.g. Rugate mirror) into single-crystalline SiC and to realize robust MEMS
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innovative high-tech technologies, advanced data analysis tools and artificial intelligence, organ-on-chip MRD models, and drug and immunotherapy testing, and will come up with innovative ideas to advance
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PhD candidate in the automated detection of measurable residual disease in hematological malignancie
important negative prognostic factor for the risk of relapse in multiple hematological malignancies and is frequently detected using flow cytometry. The aim for this position is to develop a universal