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will possess a relevant PhD or equivalent qualification/experience in a relevant field of study (e.g. data science, AI, machine learning, statistics, physics). They will be motivated to solve problems
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personal qualifications A PhD in data science, statistical genetics, quantitative genetics, bioinformatics, statistics, computer science, or closely related fields (required). Experience with large-scale
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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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training materials for research teams, focusing on data science and machine learning techniques in geoscience. Position description: PD [Research Fellow] [520112].pdf To learn more about this opportunity
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processing would be an advantage. Proficiency in statistical software (e.g., R, Python, SAS, or Stata). Experience with clinical informatics approaches (e.g., cluster analysis, machine learning, Bayesian
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the University's research culture and collaborative profile. Qualifications: PhD in Computer Science/AI or a closely related field. Extensive research experience in machine learning, deep learning, and self
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. The HDL and SPKI research groups are part of the Centre of Research-based Innovation SFI Visual Intelligence that is a center-of excellence in machine learning research. The research groups are also active
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-atomic potentials using a combination of classical and machine-learning (ML) approaches (and a new hybrid method recently developed in our group). Some of the types of simulations that will be performed
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be in close collaboration with experimental and clinical collaborators and will provide resources for large-scale data generation and full access to the latest long read sequencing technologies
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the financial sector and the economy at large. This role is ideally suited for those wishing to work in academic or industry research in quantitative analysis, particularly in the area of machine learning and