Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
the past ten years thanks to artificial intelligence, mainly in the form of deep convolutional neural networks. In parallel, functional analysis of tissue samples via novel microscopy techniques and spatial
-
for cancer research and diagnosis as well as graph neural networks for microscopy. Main responsibilities The position involves taking an active part in CMCB lab’s daily research. The succesful postdoc will be
-
Uppsala and in Sweden at large. For information about the SciLifeLab fellow program, see https://www.scilifelab.se/research/#fellows. SciLifeLab Fellows are also part of a broad national network of future
-
provides excellent access to research infrastructure, including databases, computational facilities and instrumentation, as well as to clinical materials and networks and training activities. The four
-
technologies like normalizing flows, graph neural networks, and transformers to represent distributions over trees, to improve MSC estimation. These technologies have shown significant improvements in
-
of cancer cells. The models are trained on high-throughput datasets, including metabolomics, proteomics, and transcriptomics, and constrained to align with the cell’s molecular networks. This allows us to
-
: Master’s degree in biomedicine or biostatistics. Doctor of medicine degree with clinical practice experience. Certified training in R and Python software. Documented experience using machine learning and
-
program (Data-Driven Life Science) with focus on precision medicine. Access to top-level infrastructure, a new therapy development initiative for brain diseases (CNSx3), and a strong network spanning
-
learning, deep learning and relevant software framework (R and Python) is highly desired. Very good oral and written communication skills in English are required. Emphasis will also be given on personal
-
standard Python ML libraries (e.g., PyTorch) and software development tooling (git and docker) is preferable. Experience in the application of AI and Machine Learning in the analysis of scientific data