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Professor will be responsible for teaching undergraduate and graduate courses in computer systems and algorithm design on a temporary basis. Teaching may be in our core courses alongside other faculty
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and/or machine learning Interest in biology or molecular biology, microbial ecology Proficiency in programming languages such as Python, R and/or C++ as well as Linux systems. Fluency in spoken and
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-cell transcriptomics, or spatial tissue profiling data, and are keen to develop new methods, for example using machine learning. You have a proven track record of independent research funding and high
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and data processing skills: experience of programming in one or more languages (e.g. R, C/C++, Python, Matlab). Practical experience of algorithm development and implementation of machine learning
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instructor to teach a total of six courses over the course of one year. In Summer 2026, the instructor will be responsible for teaching LA 7005, LA Design Studio NYC, and LA 7006, LA Design Seminar NYC, both
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 1 month ago
-throughput screening; - Cell painting assays and high-content image-based analysis (e.g., CellProfiler, Harmony); - Machine learning models for antimicrobial activity prediction (e.g., Weka); - Strong
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Shifting the paradigm: machine-assisted scholarly digital editing Digital Humanities Institute PhD Research Project Self Funded Dr Isabella Magni Application Deadline: Applications accepted all year
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Learning Contexts TLTED 5005 - Equity, Diversity, and Justice in Education TLTED 5108 - Teaching and Learning of Mathematics in Grades Pre-K - 5 MATH 1050 - Precollege Mathematics I MATH 1075 - Precollege
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data