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projects, including the flagship EXCELSIOR H2020 Teaming Project (https://excelsior2020.eu/ ), as well as DIGIFARM, NOSTRADAMUS, CERBERUS, and CARBONICA. ERATOSTHENES Centre of Excellence is an autonomous
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systems, understand the design and development decisions that propagate social biases, and develop theoretical and algorithmic approaches to mitigate them. Key responsibilities include developing bias
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Transfer Partnership (KTP) to develop advanced AI capabilities that will unlock the next level of market insights in the Kids, Parents and Family sector. Employed and supported by an academic team from
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biological or other research data. Utilizes and develops algorithms, computational techniques, and statistical methodologies. Helps in the design of new experiments. Implements end-user needs in database
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to the development of novel indoor localization and tracking methods, algorithms, and systems ? Evaluating the performance of such methods, algorithms, and systems via modeling and simulation ? Performance evaluation
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reactivity across diverse materials, such as catalysts, quantum materials, energy storage systems, and superconductors, you will develop and deploy cutting-edge AI/ML algorithms for real-time Extended X-ray
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validation of artificial intelligence models. Support in the acquisition and management of data collected by the group in a clinical or laboratory setting. Experimental evaluation of algorithms, development
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Doctoral student in development of nanowire devices for photonic neuromorphic computing (PA2026/472)
and analytically, to solve problems independently using the right methods, and to develop an awareness of research ethics. In addition, you will have the opportunity to work on projects, to develop your
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as of the 01.04.2026 at the following conditions: 50% = 19,92 hours Pay grade 13 TV-L limited by 31.03.2029 Your tasks: Development of architectures and algorithms for adaptation of time-triggered
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable