23 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" positions at ICN2
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the development and assessment of neurotechnology aimed for invasive brain-computer interfaces. In particular, the work will include in vitro assessment of the performance of electrophysiology neural
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integrating machine learning (ML) and molecular dynamics (MD) tools with experimental feedback, the project strives to accelerate the design of efficient and sustainable nanocatalysts, contributing
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to support future project maintenance. Agile methodologies: Actively participate in team ceremonies (Daily Stand-ups, Sprint Planning, Retrospectives). Requirements: Bachelor's Degree in Computer Engineering
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computer-based systems and the preparation of data for inclusion in lab books, presentations and publications. Maintain a hardcopy or electronic lab book · Work in compliance with relevant Health and Safety
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: MSc in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science, Gaming Engineering or a related discipline. · Knowledge: Strong coding skills in Python and knowledge in materials
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characterize new reticular materials (COFs), and use them as precursor for the generation of new polymers via Clip-off chemistry. The candidate will acquire a great experience in supramolecular, reticular and
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. Knowledge and Professional Experience: Interest of learning (S)TEM, EM related spectroscopies and in-situ techniques (use of gas and/or liquid, bias and heating STEM sample holders). Previous experience
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motivated, independent thinkers, who are well organised and willing to learn. Summary of conditions: Full time work (37,5h/week) Contract Length: Temporary (6 months) Location: Bellaterra (Barcelona) Salary
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(S)TEM. Requirements: Education: MSc in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science. Knowledge: Deep expertise in electron microscopy, particularly STEM and FIB methods
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: Education: MSc in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science. Knowledge: Deep expertise in electron microscopy, particularly STEM and FIB methods. Proven experience in