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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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knowledge of data analysis and processing, programming, and development of analytical models. Experience with databases and quantitative analysis tools. Ability to work with large datasets and computational
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120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. The department also participates in the Wallenberg AI, Autonomous
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. Participate in analyzing and disseminating data results as part of participation in group meetings. Prepare manuscripts, periodic reports, scientific papers/presentations, and grant proposals. Provide advice
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the framework of the SPIRou Large Programs. The goal is to exploit SPIRou data collected for a few tens of M dwarfs of various masses, to (i) study the radial velocity curves and filter out activity to unveil
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at the Canada-France-Hawaii Telescope (CFHT) since 2019 in the framework of the SPIRou Large Programs. The goal is to analyze existing and future SPIRou data for a number of T Tauri stars of various masses, ages
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learning, decision-making, belief updating) Coordinating large database of computational task data for harmonized analyses Modeling individual differences and within-person change over time in a longitudinal
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spectroscopy measurement at state-of-the-art large-scale facilities. You will perform quantitative data analysis using programming languages such as Python. You will perform preliminary calculations to interpret
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data storage, enabling the encoding of text, images, videos, and more into nucleotide sequences. While DNA data storage technologies are advancing rapidly, computation on large DNA-encoded databases
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FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria • PhD in condensed matter physics, materials science, or a related field; • Strong knowledge of thin-film growth techniques