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PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
Join us for a fully funded PhD position in theoretical machine learning to uncover how and why transformers work. Explore their inner mechanisms using information theory. As part of this project
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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to investigate flow-induced forces in hydraulic turbines under varying operational conditions and how these forces affect the degradation and lifetime of the machines. About the position The position is based
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strong expertise in control theory, machine learning, and probability. You will also collaborate with: Vehicle Safety Division , which applies systems engineering and human factors to improve traffic
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, accepted, or under review), 2) experience in optimization or machine learning. We would like to know where your interest lies. Therefore, you are required to submit a research statement where you describe
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commitment to lifelong learning. The department emphasizes strong collaboration between academia, industry, and society, with a clear focus on utilisation. M2 is characterised by an international environment
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-scale computational methods, and bioinformatics. The division is also expanding in the area of data science and machine learning. Our department continuously strives to be an attractive employer. Equality
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related field. Background in physics-based battery modelling and/or machine learning is considered a strong merit. Excellent communication skills in English, both written and spoken. Contract terms Full
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Merits: Experience with Matlab Prior coursework or project experience in railway mechanics Background in signal processing Knowledge of machine learning techniques Main responsibilities Your primary
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the Master’s programmes in Biomedical Engineering, Systems, Control and Mechatronics, Communication Engineering, and Electrical Power Engineering. Learn more: www.chalmers.se/en/departments/e2 Main