PhD position in Computational Genomics

Are you passionate about pushing the boundaries of genomics? We are looking for a Doctoral student to join an exciting project at the intersection of genomics and data science. The successful candidate will develop innovative methods and models to decode the language of the genome and advance our understanding of how genetic variation contributes to complex diseases. The position is based in the Computational Genomics Research (CGR) Lab, within the Data Science and AI division. About us  The Department of Computer Science and Engineering, a joint department of Chalmers and the University of Gothenburg, spans the breadth of computing disciplines. Our internationally visible research, strong industry links and diverse environment create a collaborative setting where ideas grow into real impact. At the division of Data Science and AI, we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and scientific applications. The CGR lab conducts research at the cutting edge of computational biology and bioinformatics. We aim to understand human genome variation and evolution across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods and statistical analysis (https://cgrlab.github.io/research/). About the research project  This highly interdisciplinary project focuses on studying genetic variation in noncoding regions of the human genome by leveraging evolutionary information and developing models that predict the effects of variants. We tackle this challenge via two main directions: (1) developing efficient pangenomic data structures and evolutionary models, and (2) designing deep learning architectures trained on genomic data. The lab is also open to discussing research projects based on the candidate’s strengths. Who we are looking for The following requirements are mandatory: - Strong written and verbal communication skills in English - Highly proficient in a programming language (C++, Python, Rust, …). - Demonstrated experience in one of the following areas, with a willingness to learn one another: (1) genome sequences and omics data, (2) deep learning, (3) compressed data structures for strings (4) Mathematical modeling of DNA or protein evolution. The following experience will strengthen your application: - Course work in bioinformatic algorithms, data structures and algorithms, and deep learning.
 - Experience with workflow management systems (nextflow, Snakemake), SLURM, and bioinformatics tools (aligner, variant caller, samtools, Pysam). - Experience in mathematical modeling of DNA or protein evolution. - Hands-on experience with model training (CNNs, transformers, ...) and libraries (TensorFlow, PyTorch). - Experience in software and method development.