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Field
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of an observatory of behavioural finance algorithms; 4) investigate on how NLP can understand and respond to financial issues; 5) Financial education, analysis of the impact of external variables and study of market
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strong background in mathematics and statistics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience, as well as skilled
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such as scalable identification algorithms, uncertainty quantification, and the integration of learning-based models with formal verification. We offer a supportive, inclusive, and collaborative research
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical
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visions of AI-native data systems capable of querying unstructured data declaratively, you will design and evaluate novel algorithms for extracting entities and relationships, while capturing provenance and
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Technology, Dep. ETI / Embedded Systems, we are looking for a researcher as of the 01.04.2026. Your tasks: Development of architectures and algorithms for adaptation of time-triggered systems based
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in LIBS systems.; • Develop algorithms for correction and compensation of galvanometric scanning, ensuring accurate spatial correspondence of LIBS data.; • Implement automated pipelines for LIBS data
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algorithm of the PotionDB system (https://github.com/AndreRijo/potionDB ); • Evaluate the proposed improvements in a geo-distributed context, using appropriate benchmarks; • Document the proposed improvements
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resource allocation Specific Requirements We are looking for highly motivated Ph.D. researchers with interests software, modelling, simulations, or algorithms. The applicant should hold a master's degree in
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algorithm that allows accurate simulation of fluid transport processes in porous media coupled with chemical reactions (e.g. dissolution and precipitation). The algorithm will be validated firstly against