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Field
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Centrum Wiskunde & Informatica (CWI) has a vacancy in the Machine Learning research group for a talented Postdoc/researcher (m/f/x). Job description We are looking for a motivated postdoctoral
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at NYU Abu Dhabi, candidates are expected to have a strong interest in experimental research and collaborate with applied mathematicians closely. PhD holders with a solid publication record and aims
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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the intersection of machine learning and genomics. The project involves the development and application of advanced machine learning and deep learning techniques to understand the sequence-function relationships
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Institute faculty, in areas such as: * Machine Learning and Computer Vision * Natural Language Processing and Data Science * Biomedical Informatics and Computational Neuroscience * Mathematical/Theoretical
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Physics, Electrical Engineering, Material Science or closely related field in hand
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, challenging project. Learning on the job isn't just a benefit – it's a must. Education, Qualifications and Experience Essential Criteria Applicants should hold a PhD in a relevant area of Engineering
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preferred. Proficiency in computational tools such as MATLAB, Python, R, or machine learning applications in immunology is desired. Candidates should have a PhD in Chemistry, Chemical Engineering
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, excellent communication skills, excellent computer literacy. Certifications/Licenses Required Knowledge, Skills, and Abilities PhD in life sciences. Experience with proteomics, bioinformatics, mass
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying