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programming (MATLAB and Python); knowledge of data processing; experience in mathematical modelling, especially dynamic systems and neural networks; previous experience with burned area algorithms. Contracting
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29 Mar 2026 Job Information Organisation/Company Lunds universitet Department Lund University Research Field Mathematics » Statistics Researcher Profile First Stage Researcher (R1) Application
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At the Faculty of Engineering and Science, Department of Mathematical Sciences, one or more PhD stipends/Integrated PhD stipends in inference and modeling of Quantum transduction processes
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://web.mat.bham.ac.uk/combinatorics/index.html Applicants should have a PhD (or near to completion) in Mathematics awarded or submitted at the time of taking up the position, a strong record of research and publications
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The Faculty of Mathematics and Natural Sciences is 2 months after you start your position or after the start of the research project that will lead to the PhD degree. It is a condition that you satisfy
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. At the Division of Systems and Control , we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and
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include, among others: Read scientific literature in the field of quantum communication and quantum key distribution (QKD) systems. Mathematical modeling of the quantum signals emitted by a QKD transmitter
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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | 5 days ago
funded through the EU Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Bachelor’s degree in Mathematics, Physics
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Department. Requirements 1. A PhD degree or equivalent qualification, preferably with at least 2 years of relevant postdoctoral research experience; 2. A strong background in Mathematics/Applied
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mechanisms. The objective of this PhD project is to develop AI-based surrogate models for microstructure-aware macroscopic mechanical behavior by leveraging the large datasets generated within the AMMETIS