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. Volker Hovestadt is seeking a Postdoctoral Research Fellow - Computational Genomics to join his team. The Hovestadt Lab in the Department of Pediatric Oncology at Dana-Farber Cancer Institute (https
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academic environment for a 4-year period in view of a PhD degree. You have a solid background in AI (machine learning, neural networks, reinforcement learning, dynamic modelling and/or Bayesian inference
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big-data lake ecology research using techniques from statistics and computer science (e.g., statistical modeling, machine learning, data mining). Knowledge of open science tools (e.g., Github, Zenodo
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fellowships in machine learning await. Collaborate, innovate, and thrive! PhD Fellowships in Knowledge-Driven Machine Learning in Norway (8 positions) Apply for this job See advertisement About the positions
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, or SPECT Artificial intelligence or machine learning for biomedical image analysis Python, MATLAB, C++, or similar scientific programming languages Multi-modal image registration and quantitative
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language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The evaluation of applicants primarily hinges on their documented academic
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University of British Columbia - Department of Medicine | Northern British Columbia (Fort Nelson), British Columbia | Canada | 23 days ago
–75,000 per annum plus benefits. Closing date 7 Nov 2026 Website https://ubc.wd10.myworkdayjobs.com/ubcfacultyjobs/job/UBC-Vancouver-Campus---Vancouver-BC-Canada/Postdoctoral-Research-Fellow---Human
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effectively within a multidisciplinary research environment. Experience with quantitative genetics, Bayesian methods, machine learning, large-scale genomic datasets, single-cell omics or integrative
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computational tools and interpreting performance metrics. Experience with structured and unstructured data, including parsing and processing large document corpora. Strong understanding of machine
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Cancer Research (https://gillanilab.dana-farber.org/). The Post-Doctoral Computational Biologist will work on the analysis of genomic, transcriptomic, and epigenomic datasets with the goal of arriving at