The xCTing project is a pan-European industrial-academic initiative committed to the provision of a unique and encompassing training environment required to foster a new generation of innovation-minded research engineers, that will act as catalysts in the further transformation of Europe’s manufacturing industry towards global technological leadership. There are in total 15 vacancies for early stage researchers to be hosted at 10 prominent institutes in Europe. All the recruited ESR’s are expected to obtain a PhD from an internationally respected university, build experience in communicating and disseminating their work, applying their research skills in a non-academic context and receive in-depth training in transferable skills such as commercialization, collaboration and entrepreneurship.
- Early-Stage Researchers must, at the time of recruitment, hold a Master’s degree and be in the first four years (full-time equivalent research experience) of their research careers and have not yet been awarded a doctoral degree.
- The Master’s degree must be in the discipline that is relevant to the ESR project that you are applying for.
- The researcher must not have resided or carried out his/her main activity (work, studies, etc.) in the country of his/her host organisation for more than 12 months in the 3 years immediately prior to his/her recruitment. Short stays, such as holidays, compulsory national services (such as mandatory military service) and procedures for obtaining refugee status under the Geneva Convention are not counted.
- Proficiency in English is required, as well as good communication skills, both oral and written. Successful candidates may need to provide an English test (e.g. IELTS, TOEFL, Cambridge English). You may be exempt if you are a national of a majority native-English speaking country, or have qualifications / degree that has been taught and assessed in English.
Open xCTing ESR positions
- ESR 1 (RWTH): Autonomous adaptation of CT acquisition parameters
- ESR 2 (KUL): 2D neural networks for artefact correction during CT reconstruction
- ESR 3 (UNILEIDEN): 3D neural networks for artefact correction during CT reconstruction
- ESR 4 (FHOOE): Smart and autonomous feature detection and quantification for ensemble datasets and single ensemble members
- ESR 5 (IIS): Digital twin for CT analysis chains
- ESR 6 (KUL): Determination of task-specific measurement uncertainty caused by geometrical misalignments
- ESR 7 (FHOOE): Determination of task-specific measurement uncertainty caused by physical effects
- ESR 8 (UNIPD): Enabling Industry 4.0 by fast and accurate CT metrology
- ESR 9 (VG): A-priori-knowledge enhanced CT reconstruction for fast, optimized scanning strategies
- ESR 10 (WGT): Determination of optimal scanning trajectories and related parameters
- ESR 11 (NWO-I): Fast scanning strategies for conveyer-belt setups
- ESR 12 (NWO-I): Adaptive angle-selection for in-line CT
- ESR 13 (MATER): CT-based process planning and build preparation for AM
- ESR 14 (KUL): CT-based improvement of in-process monitoring capabilities
- ESR 15 (UNIPD): CT-based adaptive advanced manufacturing and assembly chains
DEADLINE: APRIL 30 2021