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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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or pre-printed at least one primary research paper as first or co-first author You have some experience in experimental work Desirable but not required/ Nice to have A strong foundation in machine learning
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research paper as first or co-first author You have some experience in experimental work Desirable but not required/ Nice to have A strong foundation in machine learning and statistics You are experienced
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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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, 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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; 2) teach specific training courses and postgraduate studies; and, 3) disseminate knowledge concerning population in Catalonia by means of publications, seminars and lectures. The CED is a public
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, immunofluorescence, and microscopy. Strong analytical, quantitative and time-management skills. Good/excellent written and spoken English; ability to write scientific reports and papers. Strong motivation for learning
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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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of Health (ICS). A scientific environment of excellence, highly dynamic, where high-end biomedical projects are continuously developed. Continuous learning and a wide range of responsibilities within a
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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