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frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems
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técnicas de machine learning e IA. Sólidos conocimientos en análisis y tratamiento de datos, programación y desarrollo de modelos analíticos. Experiencia con bases de datos y herramientas de análisis
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microscopy. • Expertise in metabolomics with mass spectrometry is desired. • Strong general computer skills, experience with databases and scientific applications, and ability to quickly learn and master
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in privacy preserving machine learning (ML) within the SSF-ML-DH project, under the supervision of Olivier Cappé (CNRS, DI ENS) and Jamal Atif (Ecole Polytechnique, CMAP). Funding is available for two
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. The postdoctoral researcher(s) will join an international research environment at Umeå University, including Stat4Reg (www.stat4reg.se ), which develops statistical and machine-learning methods for register data
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, integrating statistical inference, machine learning, and population genetics. We will develop advanced computational methods to characterize the functioning of T- and B-cell repertoires. The goal is to build
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. Beyond Discrete Mathematics, the Department of Mathematics and Mathematical Statistics carries out research in computational mathematics, financial mathematics, mathematical modeling, analysis, machine
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that prioritise student learning in the age of AI. Job requirements You hold a PhD in Educational Science, Learning Sciences, Social and Behavioural Sciences, Engineering Education, Human-Computer Interaction or a
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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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watermarks. Publishing research in leading journals and conferences in speech, audio, and machine learning, and contributing to open-source releases of software, trained models, and reproducible research