58 computer-science-programming-languages-"St"-"FEMTO-ST-institute"-"St" PhD positions in Luxembourg
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· PhD in Aerospace Engineering, Mechanical Engineering, Applied Physics, or a closely related discipline. · Strong academic background in computational mechanics, fluid dynamics, electromagnetics
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, mathematics, physics, remote sensing and machine learning. Experience and skills · Strong interest in modelling, model-data integration, and remote sensing data analysis. · Knowledge of programming, remote
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encouraging curiosity, innovation and entrepreneurship in all areas · Personalized learning programme to foster our staff’s soft and technical skills · Multicultural and international work
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· Programming skills in Matlab, R, and/or Python · Experience in the organisation of field campaigns Language skills · Fluency in English, both oral and written. Your LIST benefits
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science or related discipline Experience and skills Experience in developing MLIPs, including good programming skills in Python and C, demonstrated via contributions to code repositories Experience with
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programme to foster our staff’s soft and technical skills ·Multicultural and international work environment with more than 50 nationalities represented in our workforce ·Diverse and inclusive work environment
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and Engineering at the University of Luxembourg, and follow a vibrant PhD program that includes training in transferable skills, support for career development, lectures and teaching by international
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Qualification: Master in Computer Science and/or Cybersecurity or equivalent degrees with expertise in at least one of the above areas (dependability, real-time systems, operating systems
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Qualification: The candidate should possess a MSc. Degree or equivalent in Engineering, Computer Science, or related fields. Experience: The ideal candidate should have some knowledge and experience
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personalized immunotherapies for cancer patients with brain metastasis. The TCI group works at the interface between immunology and cancer biology and combine the use of preclinical models of cancer and human