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representatives o carrying out data analysis and report writing. Candidates will have (or be working towards) a postgraduate degree (ideally PhD) in any of the sectors: Data Science / AI Machine Learning
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science data. For this position, we're seeking candidates with a background and expertise in Large Language Models, Computer Vision Models, Deep Neural Networks, and/or other 'AI' or Machine learning fields
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sciences, law, and philosophy. Four WPs address citizen-empowerment-scenarios (CES) in healthcare, mobility, public governance, and healthy living. Each PhD position is embedded in one work package and
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part of a team, able to learn quickly, meet deadlines and demonstrate problem solving skills. Thorough knowledge of web, application and data security concepts and methods. Preferred Qualifications PhD
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in foundational neural models that learn from large unlabeled image datasets, also incorporating context from additional data such as wireline logs or well reports. You are suited for this position
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. Strong (inter-)national network in field of application. Experience with high-performance computing (HPC) and large datasets. Experience with machine learning applied to geophysical signals. Experience in
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CIIMAR - Interdisciplinary Center of Marine and Environmental Research - Uporto | Portugal | 24 days ago
highly motivated and oriented towards research in computational biology applied to microbial genomics and natural products biosynthesis. Experience in machine learning and the application of artificial
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vision and novel applications of machine learning. Advanced knowledge of R or Python is required. Intermediate knowledge in C/C++ and/or at least one SQL dialect is preferred. Apply online at https
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application! We are looking for a research engineer within the Division of Statistics and Machine Learning (STIMA) at the Department of Computer and Information Science. In this position, you will have the
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. Additional duties may include contributing to writing grant proposals; working with large datasets; analyzing data for accuracy and data integrity; developing questionnaires for follow-up studies