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online databases or interactive websites. Learning Objectives: TUnder the guidance of a mentor, the participant will learn techniques in genomic epidemiology and machine learning to quantify drivers of IAV
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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital
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supermassive black holes through a combination of observational data, machine learning techniques, and cosmological simulations. The group is actively involved in multiple JWST Guaranteed Time Observation (GTO
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of the top private universities in the United States, and the Tandon School of Engineering, located in Brooklyn, NY, is deeply committed to excellence in teaching and learning. Tandon fosters student and
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developers and many more, that are focused on bringing data, machine learning and statistical modeling into the products that we build for our clients or internal users. The data scientists in INGA furthermore
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at the intersection between analytical chemistry, chemometrics and life sciences. As a postdoc in this project you will learn to use and help to develop cutting-edge methodologies linked to vibrational spectroscopy and
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/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data
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innovation, and translating molecular insights into functional outcomes. For consideration, applicants need to submit a cover letter, curriculum vitae with full publication list, a transcript, statement of
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/02/03 11:59PM ** (posted 2025/12/11, listed until 2026/02/03) Description: Apply Description Call for Applications: Humanities Scholars Program Postdoctoral Associates Open to Cornell PhD Candidates
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 28 days ago
based on a combination of novel simulation techniques, Bayesian statistical methods and machine learning approaches. The successful candidate will work closely with Prof. Dr. Volker Springel, the director