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- MASARYK UNIVERSITY
- Biology Centre CAS
- Biofyzikální ústav Akademie věd ČR, v. v. i.
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, • in-depth knowledge of machine vision principles and image processing, • programming experience, preferably in Python, • practical experience with the design and testing of simple electronic circuits
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). The candidate should have hands-on experience developing state-of-the-art machine learning models, particularly deep neural networks (experience with graph neural networks is highly valued). Their background
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modelling, and FGF printing and skills in using ML in analysis and design of FGF printed auxetic structures. Suitable candidates should have a Ph.D. in Polymer Materials Science, experience with electron
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Qualifiations The applicant must have: PhD in plasma physics Experience abroad or in industry Extensive experience in experimental plasma physics Solid expertise in optical or laser spectroscopy Ability to
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. Your qualifications MSc degree (or equivalent) in physics Knowledge of plasma physics is a plus Experience in programming languages (e.g. Python, C++) Proficiency in English, both written and spoken We
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educational attainment: University degree Qualification level: R1 Professional requirements: Completed university education focused on medical applications; Practical experience in biochemical, microbial, and
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will have a background in computational sciences or life sciences with a strong interest in applying these skills to environmental health research. Experience with programming, statistics, or machine
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collaboration with leading experimental teams and will be tailored to the interests and strengths of the candidate. We look forward to discussing this exciting opportunity and the project's scope further during
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technologyEducation LevelMaster Degree or equivalent Research FieldTechnology » Materials technologyEducation LevelMaster Degree or equivalent Skills/Qualifications Qualifications/experience In accordance with
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we expect MSc. degree in physics (ideally in condensed matter, materials science, or a related field) Reliability, adaptability, and hands-on experience with experimental work Natural curiosity