142 coding-"https:" "https:" "https:" "https:" "https:" "https:" "I.E" Postdoctoral positions at Nature Careers
Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
., pyTorch, TensorFlow) Proven publication record in relevant fields Familiarity with high-performance computing environments Familiarity with using and adapting open-source AI models and code repositories
-
visualization; applicants are encouraged to include links to relevant code or repositories in their CV experience working with multimodal or data‑driven research workflows and translating quantitative outputs
-
and support of users (from within and outside of BlueMat) before, during and after their beam times Scientific & technical advancement, i.e. active acquisition of scientific collaborations within
-
mission to improve lives. Our Speak-Up policy is an important part of our Code of Conduct. Only in this way we can continuously develop and improve as a company. Our core values of empathy, respect, passion
-
on ARSPECTRA hardware in collaboration with their engineering team Contribute to open-source code, demonstrators and joint publications with ARSPECTRA and clinical partners Your profile PhD in computational
-
Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
ecological processes, i.e., vertical turbulent diffusion, phytoplankton production and consumption, greenhouse gas emissions, etc., to develop hybrid models. Performance will be compared to several 1D aquatic
-
integrating biological data sources (clinical event sequences, genomic sequences, disease codes) into unified patient representations and state sequences for predicting disease progression and outcomes
-
, extensive experience with scientific coding in R and/or Python, and prior experience in applying quantitative methods and modelling to real-world questions in domains such as clinical or health psychology
-
-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and
-
epidemiology, pharmacogenomics, statistical genetics, or population genetics and experience in statistical and computational analyses of high-throughput omics data Ability to code in one or more scientific