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knowledge of mathematics (e.g. linear algebra, probability, statistics, stochastic processes) Experience of developing in larger projects While we don’t expect applicants to know biology, applicants
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postdocs will be part of the Research School. The DDLS program has four strategic research areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 2 months ago
Biology, Bioinformatics, Computer Science, Applied Mathematics, Statistics, Biophysics, Biomedical Engineering, or a related field. Strong programming skills in at least one of: Python, R, C/C++, or Perl
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. Additional qualifications Experience with one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models
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fundamental AI methods together with their software implementations for interpretable statistical fault prediction and lifetime assessment in the context of Structural Health Monitoring of operating wind
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equivalent, in bioinformatics, data science, computer science, computational biology, statistics, public health, biomedical engineering, applied mathematics, physics, or another quantitative field of relevance
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(mathematics, statistics, AI&ML, systems engineering). Supervise and support PhD students and postdocs involved in joint projects. Help establish new collaborations with industry and public-sector stakeholders
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structured training and supervision of more than 1,500 doctoral candidates in mathematics, natural and life sciences. Working on research projects towards their doctorate, the students are supported in
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Research School will be expanded with the recruitment of 25 academic and 7 industrial PhD students. During the course of the DDLS program more than 260 PhD students and 200 postdocs will be part of
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(algorithms), and statistics. During this project, you will develop new methods to construct phylogenetic networks and generalize mathematical frameworks of phylogenetic network classes to tackle related