39 parallel-computing-numerical-methods-"Prof" positions at Linköping University in Sweden
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control (MPC) under uncertainty for autonomous systems. The research aims to develop state-of-the-art numerical methods for solving challenging belief-space optimal motion planning problems and their
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. software and parallel computing is required. Skills in data processing and visualizing the results of quantum-chemical simulations are strongly desired. The candidates experienced in software
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Horizon 2020 Is the Job related to staff position within a Research Infrastructure? No Offer Description
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The Organic Nanoelectronics group, led by Prof. Simone Fabiano, at the Laboratory of Organic Electronics, Linköping University, is now seeking a highly motivated doctoral candidate to join the
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part. Your work may also include teaching or other departmental duties, up to a maximum of 20 percent of full-time. Your qualifications You have have graduated at Master’s level in Computer
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multidisciplinary research program on apocalyptic imaginaries, funded by Riksbankens Jubileumsfond. In addition, there is a project on warning sounds, air-raid sirens, and citizen information which uses historical
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application! We are looking for a PhD student in Statistics with placement at the Division of Statistics and Machine Learning, Department of Computer and Information Science. Your work assignments As a PhD
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exclusion, health and illness, and social contexts. Furthermore, ASC runs an ambitious and innovative master’s program in Ageing and Social Change. The program is unique and provides students with knowledge
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issues in federated and decentralized learning systems. The aim is to develop novel methods for securing communication against passive and active adversaries, leveraging tools from statistical estimation