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related field. Strong knowledge of machine learning. Strong publication record in a relevant field. Excellent analytical and problem-solving skills. Interest in collaborative research with both academia and
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, candidates should have completed their doctorate no more than four years before the start of employment. For well-justified reasons (e.g., parental leave, military or civil service), this limit may be extended
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more than five years ago at the time of accepting the position. In this context, the 5-year limit refers to a net period of time, which does not include maternity leaves, parental leaves, military service
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processing, machine learning, statistics or related fields. Demonstrated expertise in ML/AI, with prior experience of applications in the healthcare domain, particularly in cancer research considered a strong
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
to develop machine learning-enabled approaches for predictive modelling and state estimation for fundamental applications within physical sciences. Your role The main research responsibilities involve building
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inequalities and Sobolev-type spaces (with Hytönen and/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic
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activities. We also have access to real quantum hardware, including VTT’s Q50 and Helmi machines and Aalto’s Q20, all located right downstairs from our offices. In addition, access to other leading commercial
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(linking phenotypes, imaging, cytometry, or other readouts to transcriptomics) Statistics / machine learning for biological inference (model validation, differential state testing, embeddings/classifiers
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net period of time, which does not include parental leaves, military service etc.) good skills in spoken and written English motivation for research work in aerosol physics or chemistry. Please note
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machine learning techniques, and GPU programming. The simulation results will be compared to observational data obtained using facilities worldwide including ESO and NOT. Who we are looking for A successful