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new insights into the phenomena observed and enrich the databases required for deep learning methods. The neural networks currently being developed at LISTIC to detect and segment areas of movement in
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. In particular, he/she will be expected to :• Select and evaluate the most suitable approaches from the wide range of machine learning and computer vision methods available in the literature, with
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structure calculations, vibronic property simulations, and analyzing surface adsorption phenomena. Knowledge of machine learning potentials (e.g., GAP, ACE) or reactive force fields is a plus, as fallback
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a machine learning model (foundational model) to propose protocols of sequential induction of transcription factors to generate desired cell subtypes. The project will be conducted in close
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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also interdisciplinary knowledge on the subject. More precisely: PhD degree in computer science, machine learning, computational biology, or a closely related field Strong research track record
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Information Eligibility criteria Applicants should hold a PhD in theoretical chemistry, physics, materials science, or a related field; -demonstrate strong expertise in machine learning (regression, neural
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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models Foundation models represent a breakthrough in AI, as did the shift from traditional machine learning to deep learning. Numerous models become available in the field of Earth Observation and can be