Sort by
Refine Your Search
-
learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron Collider, with the support of competent and friendly colleagues in
-
and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
-
complex or high-dimensional systems. Experience with physics-informed or constraint-based machine learning (e.g. neural ODEs, energy-based models) Experience with dynamical systems, stochastic processes
-
, tissues, and organs using antibody-based imaging, transcriptomics, and systems biology approaches. Since its launch, the atlas has generated one of the world’s most comprehensive open resources for spatial
-
are essential to quality and form an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it’s like to work and grow at KTH. Trade union representatives
-
measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with modern deep learning frameworks (PyTorch, JAX, or equivalent). Have
-
on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
-
biomaterial incorporation, advanced imaging and staining of specific biological features. You will be responsible for designing, fabricating, and optimizing these systems using bioprinting and cleanroom-based