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sequences, and to develop new methodology to predict conformational diversity and changes using machine learning. With the help of deep-learning approaches methods to predict flexibility, conformational
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include the opportunity for three weeks of training in higher education teaching and learning. The fellow will be expected to lead/contribute to research linking vegetation modelling with the LPJ-GUESS
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at the Division of Computer Vision and Machine Learning (CVML) at the Centre for Mathematical Sciences . The Centre for Mathematical Sciences is a department affiliated with both the Faculty of Engineering (LTH
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. professional experience. Other assessment criteria: Experience working with Deep Learning and/or Computer Graphics Familiarity with Deep Learning and/or Computer Graphics research Interest in Computer Graphics
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amounts of data from different sources. - Very good programming skills - Experience in applied AI and machine-learning methods. - Good knowledge of spoken and written Swedish. Research expertise is the
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work environment and committed employees. We have a strong focus on leading research and good teaching and we have access to equipment for advanced computer calculations and experimental measurements
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languages and program analysis Good knowledge of mathematics Interest in machine learning, especially LLMs Assessment criteria Selection for third-cycle studies is based on the student’s potential to profit
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methods, such as machine learning, to study thermal runaway, venting processes, and the microscale mechanisms that can lead to battery failure. This research aims to significantly improve the predictive
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applying statistics and basic machine learning to biological data. Experience communicating with non-academic stakeholders. Experience applying for, and receiving, funding for research or educational
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(https://dune-project.org ). For Area 2: Knowledge in uncertainty quantification, computational statistics, and/or machine learning is a strong merit. An assessment of ability to think independently and to