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Field
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settings Formal semantic or symbolic methods for monitoring, evaluating, and improving LLM robustness. The positions are embedded in an active and rapidly growing research environment, including ongoing
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of the monthly maintenance allowance is € 1309,64 being the payment method by Wire Transfer. Selection methods: The selection methods will be the following: A) Adequacy and merit of the Curriculum vitae with
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formalities established therein must be fulfilled by the date of the act of contracting. Preferred conditions a) Master’s in Consumer Sciences and Nutrition or in Public Health. Functions The functions to be
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on food safety. The researcher will be responsible for planning and conducting field sampling and laboratory assays, including the development and validation of multiresidue methods by liquid chromatography
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. The successful candidate will undertake a course in legal research methods during the first year as a full-time student. This provides training in legal research methodology and supports PhD students in designing
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approximation algorithm, using linear programming methods. Over the next years, the project will grow to a collaborate team of 4-5 PhD students and Postdocs. The successful candidate will work directly with the
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inverse problems. The team aims at developing Bayesian computational methods for such (ill-posed) inverse problems and aims both at increasing their validity and at reducing their computational cost. In
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of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation of the mathematical models of the patho-physiology and PK/PD
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or transformed from other maps. The ERC funded project GeoTrAnsQData project addresses this by developing a GeoQA method that converts questions into executable geo-analytical workflows, turning geodata into new
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
materials science or a related discipline • Some knowledge of the theory of materials and experience with computational methods in materials science • Some experience with machine-learning interatomic