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computational biophysics Machine learning and data analysis for biological systems Biomedical imaging and signal processing Molecular modeling and simulations AI applications in bioinformatics or health sciences
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physical). Solid background in programming and experience with machine learning. Knowledge of participatory design and co-creation methodologies. Ability to learn independently and passion for research
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quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a welcoming and international work environment. The research group in
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execution and milestone completion. Job Requirements Strong background in AI/NLP or speech technologies, with experience in designing and implementing machine learning models. Proficient in software
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through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build healthier, more educated communities in South Carolina and
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of everyday life. This project aims to change that by developing AI-driven methods to assess wellbeing through video-based sentiment analyses. As a PhD student, you will develop and refine machine learning
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or junior graduate students. A formal training, education, or certification in a secondary area (beyond the main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning
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applications for postdoctoral or more senior research associate positions broadly in statistics and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu). The position is for one year
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eligibility, please visit: http://uhr.rutgers.edu/benefits/benefits-overview . Posting Summary Outstanding applicants are desired for a Part-Time Lecturer (PTL) teaching position as Co-Course Director for a
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duration of the PhD research. Please see the RePIM project website (https://repimnetwork.eu/recruitment ) for further information. Qualification requirements PhD stipends are allocated to individuals who