26 post-doc-machine-learning Postdoctoral positions at Chalmers University of Technology in Sweden
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-year limit can be made for longer periods resulting from parental leave, sick leave or military service. What you will do The majority of your time will be devoted to conducting research within the scope
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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are looking for We seek candidates with the following qualifications: To qualify as a post doc, you should hold a PhD degree in physics and have a strong interest in condensed matter physics. You should already
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regular basis with the experimental groups in the initiative. The position requires sound verbal and written communication skills in English. Swedish is not a requirement, but willingness to learn is
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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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. Project overview The project involves applying advanced statistical analysis, machine learning techniques, and modeling approaches such as agent-based modeling to analyze diverse climate and socioeconomic
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passive and active flow control algorithms, potentially incorporating machine learning/AI, to enhance aerodynamic performance and stall delay with rapid response times. The research is conducted in
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The position's field of research focuses on developing and implementing safe, transparent, and explainable AI systems using multimodal deep learning and Large Language Models (LLMs) for healthcare