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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 2 months ago
of novel computational approaches at the intersection of semiempirical quantum chemistry, machine learning potentials, and implicit solvation models. Requirements Experience in computational chemistry
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established lasting ties with industrial partners and created several spin-off companies. CONTEXT AND MISSION We are seeking a postdoc to join the Quantum Machine Learning team (QML-CVC) in beautiful Barcelona
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 2 months ago
across European life-science AI efforts. Requirements PhD in computational biology, bioinformatics, machine learning, or a related computational field Hands-on experience with foundation models / large
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and economy that respect people and their environment. We are looking for our next postdoctoral researcher in computer graphics and machine learning to join the Image, Data and Signal (IDS) department
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Skills: • Prior experience in quantum information, quantum computing, machine learning. • Proficiency in computer programming matlab, python, mathematica. How to Apply and required documents: • Personal CV
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: A doctorate in a Machine-Learning related field A deep knowledge of Control Theory, both classical and deep learning based A solid publication record in top level ML venues such as NeurIPs, ICML, and
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/communications, machine learning/vision, intelligent transportation systems, intelligent sensing/localization, or signal processing. In line with our Athena SWAN ambitions we especially encourage women to apply
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at the time of accepting the position (a net period of time, which does not include parental leave, military service etc.) good skills in spoken and written English prior experience and knowledge in magnetic
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boson decays, searches for supersymmetry and other new phenomena, and measurements of rare standard model processes. We vigorously pursue the use of machine learning techniques for data analysis
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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics