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Tenure-track Assistant and Associate Professorship positions in Data Science and Machine Learning...
-track) and/or Associate Professor levels with a focus on Data Science and Machine Learning. The expected starting date is summer 2026, with room for flexibility. Depending on the profile of the candidate
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teaching faculty to teach an undergraduate course, Machines that Create, an introductory yet comprehensive overview on Generative AI and Foundation Models, covering the methods and techniques driving modern
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and implement machine learning systems, models, and schemes Research and apply state-of-the-art machine learning prototypes to sponsor specific domains Search and select appropriate data sets before
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Title: Instructional Technologist - Learning Systems Administrator Position Type: Staff Full-Time Days Per Week: Monday - Friday Hours Per Week: 40 VP Area: Finance and Administration Department
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to Lancaster's high-performance computing facilities. Essential Requirements PhD in Machine Learning, Computer Science, Computational Neuroscience, or related field Demonstrable experience developing deep learning
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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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from working closely with its team of post-docs, associated researchers and partners (that range from Microsoft Research to the NHS). For this project you should have a strong interest in AI/Machine
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, using signal processing/machine learning techniques, to realise all-weather perception in autonomous vehicles with high-quality multiple-input-multiple-output (MIMO) radar sensing/imaging. The project
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PhD studentship in Machine Learning-Based MIMO Radar Sensing for All-Weather Perception in Autonomous Vehicles Award Summary 100% fees covered, and a minimum tax-free annual living allowance
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, and construction of optical systems in the laboratory Documentation and evaluation of experimental and simulation results Development and validation of machine learning algorithms (e.g., CNNs, deep