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., • Interest in developing risk prediction models via deep learning/machine learning. • Have strong background in DL, EEG data and programming for the implementation of proposed methods. Apply now
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Earth Engine, ENVI, MATLAB, or R. Desirable Proficiency in applying machine learning methods to multispectral and hyperspectral data for detecting crop diseases and estimating crop yield and quality
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and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known
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for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills Interest
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-machine teaming. Moreover, the consequences of such digital transformation are explored in the context of workforce and labor developments, organisational innovation, and learning/education. About the
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transformations. Combining automated reaction discovery simulations, microkinetic modelling, machine learning, and global optimization, this project will develop a new and exciting route to addressing one
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will be advantageous. Knowledge of machine learning or reinforcement learning techniques will be advantageous. Proficiency in algorithm development using Python will be advantageous
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four years are expected to acquire basic pedagogical competency in the course of their fellowship period within the duty component of 25 %. Place of work is Department of Chemistry at Blindern/Gaustad
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computers”(under the UKRI Guarantee scheme). Topics include: - Quantum many-body dynamics - Quantum algorithms - Quantum-enhanced numerical methods - Quantum machine learning - Tensor Networks - Topological
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mapping tools such as Google Earth Engine or Microsoft Planetary Computer, and it is likely that developments such as satellite data embeddings and GeoAI will offer potential for increasing the accuracy and