13 machine-learning-phd scholarships at Stockholm University

PhD student in Application of Machine Learning to Advance Bathymetric Seafloor Mapping and Data Compilation

been directly mapped. This PhD position at Stockholm University focuses on leveraging machine learning (ML) to identify errors in large bathymetric datasets and applying ML techniques like "super

PhD student in Data Driven Evolution and Biodiversity: Plant Extinction

, probabilistic modelling, machine learning and computational approaches in biodiversity science. The student will be will be supervised by Aelys Humphreys (Stockholm University) and work closely with

PhD student in Data Driven Evolution and Biodiversity: Plant Extinction

, probabilistic modelling, machine learning and computational approaches in biodiversity science. The student will be will be supervised by Aelys Humphreys (Stockholm University) and work closely with

PhD student in Marine Geology: Arctic Ocean borealization during past warm climates: signals from planktonic foraminifera

biostratigraphy, Quaternary Arctic palaeoceanography, marine sediment sampling methods and geochemical analysis within a network of international collaborators. AI/Machine learning approaches are also possible

PhD student in Bioinformatics

. In this project, we are recruiting additional Ph.D. student to leverage recent advances in machine learning to create better deep-learning models to predict protein-protein interactions and to apply

PhD student in High resolution Mass spectrometry

) consolidator grant. Project description Project title: Pinpointing toxic chemicals from mass spectrometric data with supervised and unsupervised machine learning. Supervisors: Assoc. Prof. Anneli Kruve (main

PhD student in Neurochemistry

such as dimensionality reduction, clustering and visualization in combination with advanced tools of machine learning and neural networks to build models of epigenetic regulation of gene expression during

PhD student in Biochemistry

approaches (QM/MM, molecular dynamics, free energy methods, machine learning). The PhD candidate will work closely with other PhD students, postdocs and senior scientists of the lab in an interdisciplinary

PhD student in Biophysics

approaches (QM/MM, molecular dynamics, free energy methods, machine learning). The PhD candidate will work closely with other PhD students, postdocs and senior scientists of the lab in an interdisciplinary

PhD student in AI for Drug development

between these. There will be a strong focus on developing machine learning tools and novel molecular representations. Fundamental knowledge of machine learning and programming as well as molecular biology