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
-
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
-
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
-
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
-
will use machine learning methods to develop affinity ligands. These methods have been transformative for protein design, allowing generation of novel proteins which can suit a precise need. In this 4
-
delimitation in biodiversity conservation using modern artificial intelligence (AI) methodologies. Based on morphological traits, traditional species delimitation methods struggle with groups like bacteria and
-
cell cultures on chip. The purpose is to develop in vitro models of solid tumors that can be used to study the tumor microenvironment, and also to investigate whether acoustofluidic methods can be used
-
of the leading units in the area in Sweden with particular strengths in nutritional and computational metabolomics, dietary biomarkers, micronutrient metal nutrition, nutritional immunology, marine food science
-
various environmental samples ranging from extinct and ancient animals to consumer food products. These fragments can be detected and analyzed using our specialized wet and dry-lab methods. The objective
-
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