Jobs

The Yang Zhang Lab at the National University of Singapore (NUS) is recruiting two postdoctoral fellows in artificial intelligence and computational biology. The lab is jointly based in the School of Computing, the Yong Loo Lin School of Medicine, and the Cancer Science Institute of Singapore, working at the interface of computer science, structural biology, and biomedical research.

The positions focus on developing next-generation AI methods for biomolecular modeling and AI-driven drug discovery, including protein/RNA folding, structure prediction, and sequence design. The research is method-driven, emphasizing original algorithm/model development (foundation-model architectures integrating sequence, structure, and physical information) rather than applying existing pipelines to standard benchmarks.

Fellows will work in a computationally intensive environment with access to large-scale HPC (hybrid NVIDIA GPU/CPU clusters) and are expected to lead projects from conception through large-scale training and rigorous evaluation.

A 2-year (renewable) full-time postdoctoral fellowship is available in the laboratory of Prof. Alain Dabdoub. The position focuses on decoding the molecular landscape of the human inner ear during development and regeneration and translating these findings to advance therapeutic strategies. Our research investigates mechanisms that regulate auditory and vestibular homeostasis, the regeneration of sensory hair cells, stria vascularis, and auditory neurons as well as the implications for therapies and understanding disorders associated with aging, hearing loss, and balance dysfunction. Our projects combine state of-the-art computational tools and cutting-edge biological experiments.

Background

The Personalized Approaches in the Treatment of Head and Neck Cancer (PATH) study aims to use longitudinal plasma cell‑free DNA sequencing to develop a minimally invasive method for detecting minimal residual disease (MRD) and recurrence in head and neck squamous cell carcinoma (HNSCC). Approximately 35 % of patients with HNSCC recur and die despite aggressive treatment. There are currently no validated biomarkers to monitor treatment response or detect residual disease. The proposed pilot will collect serial blood draws from 30–50 patients per year and perform whole‑genome sequencing (WGS) on Illumina and Oxford Nanopore Technology (ONT) platforms. It will integrate genomic, epigenomic and fragmentomic features (SNVs, copy‑number variants, methylation marks, fragment size, end‑motifs, nucleosomal positioning, viral integration) to sensitively detect low tumour fractions. The project’s third aim is to explore bioinformatics tools to develop a genome‑wide multimodal MRD analysis pipeline, analyze baseline plasma samples, and retrospectively analyze longitudinal samples to detect tumour burden. This position will be central to achieving those aims.

High‑level Summary of the Role

The post‑doctoral fellow will lead the development of an integrated analysis pipeline for detecting minimal residual disease in head and neck cancer patients by analyzing longitudinal plasma ctDNA. Using whole‑genome sequencing from both Illumina and Oxford Nanopore platforms, the fellow will combine genomic mutations, copy‑number alterations, methylation signatures, fragmentomics and viral integration data to sensitively track tumour burden over time. They will adapt and create bioinformatics methods, apply tumour‑informed analyses using matched tumour and normal WGS, and analyze baseline and follow‑up blood samples to identify early ctDNA signals before clinical standard of care recurrence. The fellow will collaborate within the GSC and PATH project to produce reports and manuscripts, and help scale the pipeline for future studies to impact cancer patient care.

The Pouget Lab is seeking a Postdoctoral Fellow in computational genomics and data science to study how genetic variation shapes neurodevelopment and vulnerability to psychosis.

Our lab integrates large-scale human genomic data with multimodal, single-cell resolution molecular profiling of brain and peripheral immune cells to (1) discover molecular mechanisms of psychiatric disorders, (2) refine biologically grounded disease subtypes and therapeutic targets, and (3) develop scalable biomarkers for precision psychiatry.

Ongoing projects include:

• Predictive modelling of psychosis symptom trajectories in large youth cohorts
• Integrative analysis of multimodal sequencing data, including paired single-nucleus RNA-seq + ATAC-seq from postmortem human brain and peripheral blood samples
• Whole genome sequencing in clinical populations to inform genetic testing guidelines

TRAINING ENVIRONMENT
As a post-doc in the Pouget Lab, you will work within a highly collaborative, multidisciplinary research program with direct clinical translation to youth mental health. Our lab offers a collaborative, people-first, mentorship-focused environment committed to rigor, reproducibility, Equity, Diversity, and Inclusion, and sustainable research careers.

We are based at the Centre for Addiction and Mental Health (CAMH), Canada’s largest mental health teaching hospital and one of the world’s leading research centres in its field.  As Canada’s leading mental illness research facility, CAMH’s Campbell Family Mental Health Research Institute (CFMHRI) is home to scientists, staff and research trainees conducting leading research to improve the understanding of the brain and the causes, the biomarkers and cures for mental illness as well as research to advance the prevention and treatment for mental illness and addictions across the lifespan. CAMH is a fully affiliated teaching hospital and research institute of the University of Toronto.

The Morrissy Lab is seeking a highly motivated scientist with expertise in bioinformatics and data science to join our team as a Postdoctoral Associate focused on understanding immunotherapy responses in solid tumors. The candidate will use state-of-the-art analytic approaches to investigate multi-omics data and derive an in-depth understanding of tumor ecosystem dynamics in clinical samples from immunotherapy trials – including phenotypic and functional cell heterogeneity, tumor and microenvironment cell niches, and cellular crosstalk. This research is primarily focused on cohorts of single-patient and phase I clinical trial participants treated with CAR T-cells (see Zemp el, 2024, in revision at Nature; preprint on medRxiv), and identifying mechanisms of immunotherapy response and resistance from longitudinally-collected blood and tumor tissue samples. The candidate will employ innovative bioinformatics and machine learning approaches to integrate single cell and spatial datasets from clinical samples with data from ongoing preclinical studies, further incorporating genomics and proteomics profiles from in-house or public repositories.