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, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from nonlinear control and optimisation
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Europe | 9 days ago
-light scattering on coupling in MCFHost institution: CNRS-PhLAM, FranceSupervisors: Prof. L. Bigot (CNRS-PhLAM), Prof. Y. Quiquempois (CNRS-PhLAM)DC 4 – OpenProject Title: Innovative MCF amplifier
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Financing yes Type of Position Full PhD Working Language German English Required Degree Master Areas of study Husbandry, Agricultural Science, Geosciences, Environmental Science, Nature Conservation
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Financing yes Type of Position Full PhD Working Language German English Required Degree Master Areas of study Husbandry, Agricultural Science, Geosciences, Environmental Science, Nature Conservation
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to the project. Degree transcripts/certificates and, if English is not your first language, a copy of your English language qualification if completed must be uploaded. Contact Details Prof. G.Tasca, giorgio.tasca
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. Qualifications: Background in data science Interest in physics-informed machine learning Strong written communication skills Enthusiasm for interdisciplinary collaboration Strong team spirit Experience with metal
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will be able to apply for this scholarship at the same time you apply for admission to a QUT Doctor of Philosophy In your EOI, nominate Assoc Prof Lazzarini as your proposed principal supervisor, and
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Junior Research Group “ Probabilistic Methods for Dynamic Communication Networks“ (Head: Prof. Dr. B. Jahnel) starting as soon as possible. The position is within the Math+ project "Information Flow
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heterogeneity by migrant background, thereby enabling more accurate projections of disease dynamics and disease burden in different segments of the population. You will join the DynaMIGs-team, coordinated by prof
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the Leibniz Junior Research Group “ Probabilistic Methods for Dynamic Communication Networks“ (Head: Prof. Dr. B. Jahnel) starting as soon as possible. The position is within the Math+ project "Information Flow