25 physics PhD scholarships at SciLifeLab

PhD student in structure-based drug design with focus on machine learning (DDLS Research School)

of the PhD project is to develop computational approaches to identify small molecule and peptide ligands that target therapeutically relevant receptors, with the vision to accelerate the drug discovery process

Doctoral (PhD) student position deep learning modeling of drug responses in cancer

, and we train them on extensive high-throughput datasets including metabolomics, proteomics, and transcriptomics. By aligning the model predictions with physical interactions between biomolecules, our

PhD student in Biophysics

challenge. The research group of Emil Marklund investigates how sequence information in biological macromolecules governs recognition, binding, and dynamical structure. This project aims to build physical and

Doctoral (PhD) student position learning modeling drug responses cancer

-throughput datasets including metabolomics, proteomics, and transcriptomics. By aligning the model predictions with physical interactions between biomolecules, our approach ensures that the results are both

PhD position in artificial intelligence and drug resistance (DDLS Research School)

must hold an M.Sc. degree in a relevant field, such as data science, bioinformatics, mathematical statistics, mathematics, computer science, physics or molecular biology (obtained latest October 1, 2024

Doctoral (PhD) student position in Data-driven precision medicine and diagnostics (DDLS Research School)

of life and includes physical impairment, cognitive impairment, and fatigue. This PhD project aims to address key research questions to unravel the complexities of genotype-phenotype relationships and the

PhD student within Multi-Modal Modeling of Spatial Biology Data (DDLS Research School)

requirement for English equivalent to English B/6. Selection In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, candidates

PhD student in Bioinformatics

the first and second cycles of at least 90 credits in either, a) Chemistry/Molecular Biology/Biotechnology, or b) Computer Science/Mathematics/Physics and at the second cycle level, 60 credits in Life Science

PhD student in computational microbial ecology

on microbiome data from different terrestrial environments, and evaluate how to combine this information with soil chemical/physical and climatic data to predict the degree of a soil’s capacity as a source or a

PhD student in computational microbial ecology

reconstruct the nitrogen (N) cycle based on microbiome data from different terrestrial environments, and evaluate how to combine this information with soil chemical/physical and climatic data to predict