Sort by
Refine Your Search
-
around understanding how species interactions change over time and space, with a focus on butterfly caterpillar-plant interactions and development of phylogenetic methods. The EvonetsLab is supported by a
-
boundaries creates synergies that drive research forward and the high-tech methods enable research that would otherwise not be possible in Sweden. At SciLifeLab, we do not only apply the latest and most
-
chloroplasts. This project will develop and apply new synthetic biology methods with the aim to enhance the rate of carbon fixation and conversion in cyanobacteria. Such methods include, for example, CRISPR/Cas
-
factor – DNA binding, detecting protein – protein interactions or enzyme optimization. Main responsibilities The candidate will use and develop methods within one, or preferably multiple, of the following
-
Spatial metaTranscriptomics methods and thus also handling of image data. The PhD student will interact with other team members to a large extent. For this purpose, we are looking for a PhD student with
-
data as well as other modalities (i.e. microbiome) generated using the Spatial metaTranscriptomics (SmT) method. In addition, the PhD student will also apply Spatial Transcriptomics and single-cell
-
evolutionary analysis. A central component of the research will be to develop machine learning and deep learning methods trained on coding sequences and protein structure to extract patterns in data and to draw
-
delimitation in biodiversity conservation using modern artificial intelligence (AI) methodologies. Based on morphological traits, traditional species delimitation methods struggle with groups like bacteria and
-
synthesize scientific literature Experience with HPC, and clinical/patient data Worked with machine-learning methods and omics data integration Knowledge of bioinformatics pipelines (e.g., Nextflow, Snakemake
-
of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as