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about biology and interested in building new machine learning and AI to accelerate biological discoveries? Then this position is for you! Join Us! We are looking for a motivated PhD candidate to develop
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through the development of cutting-edge advanced microscopy techniques. Its main focus lies in interdisciplinary research at the intersection of photonics and biology. Main Tasks and Responsibilities Design
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and accelerate breakthroughs in medicine, biology, and environmental health. Our interdisciplinary team of scientists and engineers from more than 25 countries develops technologies that bridge
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | about 1 month ago
preserved at ambient temperature provided it is protected from light and humidity. Goal The goal of the project is to develop an algorithm to allow robust retrieval of data in the context of DNA-based data
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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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their commercialization and implementation into clinical practice. ICCVS works on a range of national and international research projects concentrating on cancer biology and novel approaches to immunotherapies. The focus
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 1 month ago
the application of various ML algorithms, including supervised algorithms for environmental predictions, as well as unsupervised algorithms for clustering and LLMs/Transformers Experience with sequence data
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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to accurate sample reconstructions using advanced signal processing and tomographic reconstruction algorithms. With the inclusion of noise the object estimation accuracy will be based on statistical concepts
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interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured