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‑of‑the‑art computational featurisation with experimental reaction‑kinetics data to build a machine‑learning platform capable of predicting catalyst performance. This is an exciting, highly collaborative
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problems, statistical learning and machine learning (machine learning, deep learning) - Knowledge of associated software development tools and environments: Python, PyTorch, Scikit-learn, Jax, Julia
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recruited candidate will contribute their expertise to the initial training of engineering students and master's students by teaching in the following areas: • Bioinformatics, • Machine learning and pattern
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strengths in experimental soft condensed matter physics or biophysics research within the department. Candidates with expertise in computational physics, including machine learning, applied to study soft
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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biology/bioinformatics, statistics, machine learning or related field. You will have a strong track record of applying genetics-based, physicochemistry-based and structure-based computational or statistical
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-computer interaction (UX), and/or appli cation of Machine Learning. • Sense of responsibility and ability to communicate and integrate into multidisciplinary work teams. Financial component
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computing systems design and realization, including machine learning (ML) and artificial intelligence (AI) applications including autonomy, sensing and communication, advanced manufacturing, and decision
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Candidates MD, DO, MBBS, PhD, EdD, or equivalent in a related field such as the health sciences, education or other field given context of work experience and/or other qualifications. Qualified for a faculty
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/training. Preferred Qualifications: Demonstrated skills (or ability to learn quickly) in any of the following: programming (especially Python), data science, machine learning, and statistics. Previous