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Description About the NeuroNanotech Doctoral Network NeuroNanotech is a doctoral network funded by the European Marie Skłodowska-Curie Actions (MSCA) that will train eleven researchers to tackle one
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databases regarding the thermomechanical behaviour of refractory masonry in the context of steel ladles used in the metallurgical industry; Development and validation of finite element models, considering
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work independently and develop your ideas. A close-knit community of colleagues to collaborate and grow with. A solid pension plan via ABP, company fitness schemes, and access to various university
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high potential for progression. The aim of the project is to develop an innovative in vitro model enabling investigation of the (micro-)invasion process in DCIS and identification of molecular markers
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Analytical Food Chemistry at Technical University of Munich | Freising, Bayern | Germany | about 1 month ago
positions for Doctoral Candidates (DCs). We aim to create a network of early-stage researchers equipped with knowledge and skills - including entrepreneurship –to develop and support implementation strategies
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Additional information on beginning, duration and mode of study The starting date of the PhD project is flexible. The initial funding period is usually three years. Find out more at https://www.bgc-jena.mpg.de
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, technology development, and bioinformatics at VIB-UAntwerp Center for Molecular Neurology (https://cmn.sites.vib.be/ ), at VIB (https://vib.be/ ), and at UBirmingham (https://www.birmingham.ac.uk
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their personal skillset to the discovery, development, and commercial translation of new 3D nanoscale magnetic metamaterials. What You’ll Do in this Project Size, weight, and power: the future is small
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on Kerberos, LDAP e Debian servers. Develop knowledge about the communication tool “Mattermost” and develop extensions for it. Development of new methodologies to optimize system administration, as
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typical residential setting) based on human-provided instructions. This includes the development of an interface to supply the robot with prior knowledge about the environment. The fellowship holder will