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for gene regulatory networks, single-cell multi-omics integration, spatial omics, and variant effect mapping in complex disease. Strong method/tool dev experience required (Python/R, ML/stats
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knowledge of programming, including Linux, Python, and R. Candidates having background knowledge in neuroimaging, machine learning, and/or genomics/genetics are encouraged. Excellent communication and writing
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/ ROS 2 for real-robot software integration. Proficiency in Python and C++, with good software engineering practices (version control, testing, maintainable code). Familiarity with robotics simulation
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. Proficiency in full-stack AI development - front-end and back-end (e.g., Python, React, Vue, Node.js, Typescript, API's, databases, Git/GitHub, CI/CD, AI Coding Assistants, Infrastructure as Code
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communication skills in written and spoken English. Proficiency in programming with high-level languages like Python. The following will strengthen your application: Familiarity with open quantum systems
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learning and deep learning Excellent programming skills in Python Practical experience with PyTorch (preferred) and/or with TensorFlow, scikit-learn, and GitHub Experience with scientific experimentation and
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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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, including languages such as Java, C/C++, C#, Python and JavaScript, as well as web technologies including HTML, CSS and AJAX/JSON-based APIs. Experience with database systems, including relational and non
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Singapore English Mental Lexicon project is available at https://langcomplab.github.io/singlish.html Information about the department is available at: https://fass.nus.edu.sg/psy/ Qualifications Candidates
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AI and in silico methods (Ref. LISBOA2030-FEDER-00719600; 2023.18347.ICDT and DOI https://doi.org/10.54499/2023.18347.ICDT ), co-funded by Lisboa2030, Portugal2030 and by the European Union and by