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- Institute of Photonic Sciences
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- ICFO - The Institute of Photonic Sciences
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The applicant should demonstrate evidence of a track record in peer-reviewed publications as well as ability to initiate and complete research characterized by originality, creativity and Applicants should have
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the postdoctoral contract. Applicants must possess a high-quality research trajectory and a competitive publication track record. Skills: RNA biology. Cell biology. Molecular biology. Bioinformatics. Experience
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Mitigation Accelerated by Photons group led by Prof. Dr. Pelayo García de Arquer is searching for exceptional researchers and engineers, with an excellent proven track record, to develop efficient water
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. F. Pelayo García de Arquer who is searching for exceptional researchers, with an excellent proven track record, to work on the development of recyclable energy conversion devices. The successful
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strong background in biophysics, physics, physical chemistry, bioengineering, or a related field. The candidate should have a proven track record in one or more of the following: Experience with optical
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/ Engineering. The candidate should have a proven track record of work on quantum information theory. LanguagesENGLISHLevelExcellent Additional Information Benefits ICFO is an equal opportunity employer
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track record of work on quantum cryptography and quantum information theory.. LanguagesENGLISHLevelExcellent Additional Information Benefits ICFO is an equal opportunity employer. Candidates are selected
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assessments Where to apply Website https://sl1nk.com/lKt56 Requirements Research FieldNeurosciencesEducation LevelPhD or equivalent Skills/Qualifications PhD in Biomedicine, or a related field. Proven track
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(ImageJ/Fiji) · GBM biology knowledge · Experience of supervising student research projects · Evidence of a developing track record in publishing and dissemination of high-quality publications in peer
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: Define and track metrics (MAE on Hamiltonian elements, band-structure error, DOS overlap, transport-relevant figures); compare against classical parameterisations. Uncertainty and active learning