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- Universitat de Barcelona
- Centre for Genomic Regulation
- Computer Vision Center
- Fundació Hospital Universitari Vall d'Hebron- Institut de recerca
- INSTITUTO DE ASTROFISICA DE CANARIAS (IAC) RESEARCH DIVISION
- Institut de Físiques d'Altes Energies (IFAE)
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- Institute for bioengineering of Catalonia, IBEC
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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scientific infrastructure (e.g. Observatorio del Roque de los Muchachos) Hands-on training in cutting-edge techniques, from detector R&D to advanced data analysis and machine learning. Attendance
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, computer science, bioengineering, data science, or a closely related discipline. • Demonstrate advanced proficiency in artificial intelligence and machine learning, particularly in applications involving
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atmospheres and detectability studies Model development of 3D stellar atmospheres Applications of machine learning and AI to exoplanet data analysis Biomarkers and habitability of Earth-like planets Where
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environments, specifically Computer Vision, Machine learning algorithms and methods for rock characterization, fragmentation prediction, and mining optimization. Specific Requirements Good academic and
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quality alarm protocols based on machine learning: thresholds, alert workflows, and response or shutdown measur. Analyze data (time series) and develop quality indicators to support municipal decision
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, transcriptomics, proteomics), machine learning, statistical analysis and programming languages such as R or Python. - Experience in image analysis, including development of custom ImageJ plugins and workflows
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. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group: Atomistic & Molecular Modelling for Catalysis Group Requirements Specific Requirements PhD in Chemistry
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internal reports and manuscripts. Requirements: PhD in Physics, Materials Science, Computational Science/Engineering, Computer Science, or related. Solid knowledge of machine learning, including graph neural
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transcriptomics data analysis. Experience in quantitative image analysis, computer vision, or digital pathology. A strong background in cancer biology or immunology. Experience with machine learning, deep learning