13 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" positions at ICN2 in Spain
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to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and electronic
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Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
Personal Competences: Demonstrated competitive ability in using DFT simulations, and machine learning techniques and DFT. Demonstrated strong coding skills and a passion for UX/UI design. Summary
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valued. · Knowledge of chemical reactions and how to model them through computer simulations is highly valued. · Knowledge of classical molecular dynamics, including Machine Learning Interatomic Potentials
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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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. 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