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problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control
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problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control
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in bioinformatics, machine learning, single-cell omics, statistics, or genomic medicine, and a keen interest in obesity, diabetes, and cardiovascular disease. Background The Novo Nordisk Foundation
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probabilistic frameworks. Experience with machine learning or AI methods for localization or perception (e.g. learning-based SLAM, data-driven sensor fusion) is a plus. Underwater or field robotics experience
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at the top venues of machine learning research. Responsibilities and qualifications You should have prior experience with machine learning from both a theoretical and practical perspective. Experience in one
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catalysts for the synthesis of a range of industrially valuable compounds. This PhD project is part of the Horizon Europe Marie Sklodowska-Curie Action (MSCA) doctoral network (DN) ELEGANCE (machinE LEarning
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polar orbit, passing near the poles about 15 times per day and regularly observing the CIFAR study region. Its payload - two optical cameras, a thermal camera, and onboard machine-learning capabilities
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) ELEGANCE (machinE LEarning for inteGrated multi-parAmetric eNzyme and bioproCess dEsign), and it will focus on: Expression, characterization and application of enzymes from University of Turin and other
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robotic research platform and an automated ‘Device Doctor’ for perovskite solar cells. The goal is to combine high-throughput experimentation, machine learning, and advanced modeling to accelerate device
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Position as Computational Analyst / Bioinformatician in RNA Therapeutics and Cardiometabolic Disease
. Proficiency in at least two of the following programming languages: Python, R. Experience in Machine Learning and Computational RNA Biology are desirable. Hands-on experience or understanding (the limitations