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Join us in designing stable materials for sustainable energy devices with machine-learning-accelerated simulation and modeling. Work assignments The postdoctoral researcher will develop machine
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interpreting results, and benchmarking brain-inspired architectures against conventional machine learning methods. You are also expected to actively participate in academic discussions with supervisors at KTH
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advanced analytical approaches, including statistical modelling, machine learning, and/or genetic epidemiological methods, to study disease trajectories, risk factors, treatment patterns, and sources
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the application of machine learning), and optimisation of power systems for grid operators and energy producers The scope, depth and quality of the applicant’s scientific work The ability to independently conduct
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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. Beyond Discrete Mathematics, the Department of Mathematics and Mathematical Statistics carries out research in computational mathematics, financial mathematics, mathematical modeling, analysis, machine
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patients with pancreatic cancer and other aggressive tumor types. The work includes statistical modelling, machine-learning and systems biology approaches to identify drivers of metastasis and treatment
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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dynamics simulations and machine learning methods to study the structure and electrochemistry of disordered materials are also encouraged to apply. The project primarily aims to understand the complex