Responsibilities

* Develop AI/ML techniques tailored towards diverse and complex molecular data types to uncover structural and functional insights.

* Develop analytical methods to process multi-omics datasets including single cell genomics, spatial transcriptomics, open chromatin, epigenomics, metabolomics, etc.

* Apply statistical techniques and data mining approaches to extract meaningful patterns and correlations from large-scale biological data sets.

* Integrate advanced visualization methods and AI/ ML algorithms for data interpretation.

* Communicate the results, interpretations, and conclusions of analysis to stakeholders and suggest relevant/ potential experiments for confirming computational analysis-based predictions.

Qualifications * Ph.D. in Computer Science, Bioinformatics, Statistics, or related fields with demonstrated experience in NGS and Multi-Omics data analysis.

* Demonstrated experience working with high throughput sequencing datasets including RNAseq, epigenomics, single-cell and spatial genomics datasets.

* Strong background in machine learning, deep learning, and statistical analysis, with demonstrated experience in applying these techniques to biological data.

* Proficiency in programming languages commonly used in bioinformatics and machine learning, such as Python, R, and TensorFlow/PyTorch.

* Experience in additional programming languages and technologies is a plus.

* Experience in computational proteomics/ metabolomics data analysis tools.

* Demonstrated experience in high performance computing and cloud technologies including AWS, Kubernetes

* Excellent written and oral communication skills with the ability to communicate complex, technical information in a clear and concise manner.

* Proven record of publishing scientific work in high-impact journals is preferred.

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