1,469 evolution "https:" "https:" "https:" "https:" "https:" "https:" "University of Texas at Austin" positions at Nature Careers
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Discovery group of Professor Reinhard Maurer. This project will focus on the development of novel machine learning representations of electronic structure and quantum operators. These surrogate models will be
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. Responsibilities and Opportunities for Skill Development Development of optical high-throughput assays for functional screens to dissect GPCR signaling Using fluorescence and FRET/BRET-based reporters of GPCR
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development of novel technologies to study this problem. We are especially interested in growing our expertise in epitranscriptomics, RNA biology, single-cell/spatial approaches, microenvironment, and
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while driving strategic development and scientific progress across its remit. Why AFBI? This is an extraordinary opportunity to lead a world-class Institute that delivers meaningful scientific impact
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aims to deliver world class regulation and improved outcomes for UK patients. We are currently looking for an experienced Principal Scientist (NMR) – Molecular Analysis to join our Research & Development
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
groups and QGG Mentorship and support in developing independent research directions Support for grant writing and professional development a workplace characterised by professionalism, equality and a
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by a thesis committee, participate in scientific and transferable skills courses and profit from career development support. The city of Konstanz is located directly on the border to Switzerland and
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Medical School-affiliated hospitals. Responsibilities: Evaluation of multi-cancer early detection tests and implementation strategies in clinical and population settings Development and testing
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team Assist in the development and optimization of clinical decision support systems (CDSS) Assist in the use of existing data analytics platforms to analyze research objectives Data Analysis & Reporting
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. Côte d’Azur & INRIA), will be focused on the development and the understanding of deep latent variables models for unsupervised learning with massive heterogenous data. Although deep learning methods and