40 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Pennsylvania State University
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Scientific Machine Learning. The successful candidate will develop and deploy state-of-the-art SciML algorithms in high-performance computational physics codes. We accept applications from all candidates with
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, Robotics, Computer Vision, or related disciplines. Proven expertise and hands-on experience in one or more of the following areas: large language models (LLMs), end-to-end learning, AV localization
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fellowship, interest in expanding or learning new research skills, how interests align with CGNE and CON, identified primary mentor and alignment with their program of research. Copy of 1-2 published articles
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calculations Materials modeling/electronic structure calculations Machine Learning/Deep Learning techniques. Education and Experience: A PhD in physics, astronomy, or a closely related field must be completed
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the brain. We are particularly looking for a PhD level systems neuroscientist with expertise in animal behavior tracking using deep learning algorithms and its causal link with specific neural circuits
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, Astronomy, or a closely related field is required. Experience with HPC systems, machine learning, and GRB monitor data analysis would be an advantage. Additional Information Applications must be submitted
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enthusiastic and ambitious to learn. Expected proficiency in atleast 4-5 of these techniques: Hematopoietic cell flow cytometry immune-phenotype characterization Transduction or primary leukemia and
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anatomical and functional investigation of the cerebrovasculature is highly desirable. However, candidates with other experimental and/or data skills are welcome to apply. Experience in computer programming
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. Familiarity with a variety of HVAC system types and current ventilation/IAQ standards is required. Proficiency in computer simulation of building energy and/or indoor air quality is strongly preferred. (e.g
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students, and may also teach one course per year for the Department of Statistics. A Ph.D. in Statistics, Biostatistics, Machine Learning, or a directly related field at the time of appointment is required